chemical_formula_hill
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int32 | software
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bool | electronic_band_gap
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string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
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string | property_object_last_modified
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string | configuration_metadata
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list | configuration_names
list | configuration_dataset_ids
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timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
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string | dataset_elements
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int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | dataset_energy_mean
float64 | dataset_energy_variance
float64 | dataset_formation_energy_count
int64 | dataset_last_modified
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string | dataset_total_elements_ratios
list | dataset_license
string | dataset_links
string | dataset_doi
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string | dataset_id
string | dataset_extended_id
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Mo32 | Mo | A | [
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| true | null | null | -316.174927 | null | 0.714095 | 1.651907 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:32:39 | 6527264832586919342410846300349363159847738879059124073763622685265119197248809116505183588908855353381285742339959799377510247784048451591274984863782867 | PO_6527264832586919342410846 | {"materials-project-id": "mp-8637", "hash": "6966233607090115772682513866328493864257802604788524296728866462601494288840305587996997246366084672544881309975060598897334822454883723648657276032455554", "id": "MD_6966233607090115772682513"} | MD_6966233607090115772682513 | null | [
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| [
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| 2023-12-01T22:55:06 | 12606842638188908149627855096569387390178608122710939456185242561109465966289889340781445220930376523522523244138203524876627828561256877696840578721566346 | CO_1260684263818890814962785 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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"Wissam A. Saidi"
]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| 2023 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
Mo32 | Mo | A | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -74.474037 | null | 3.203268 | 4.601943 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:40:46 | 12905467436892173562207467240512592197142472298393748121045064151877898455952369782985484921577002272088622529479090344177985662041796589781769357322163004 | PO_1290546743689217356220746 | {"temperature": 2000, "hash": "5038272108731326564792169255289409101806296861397970431569880381208879553004280715011540754918172690659147781535715733200062875503570190465144527282590642", "id": "MD_5038272108731326564792169"} | MD_5038272108731326564792169 | null | [
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| 2023-12-01T22:55:06 | 3886930020026832290581450424035329318088309996407530372965734919856241767569190543078964500099542268152047499214884910411706120452742838778980913124882158 | CO_3886930020026832290581450 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -235.228683 | null | 4.563438 | 7.929487 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:44:59 | 91314754988153607473071440672337432876250849022202016433491235122389552531660127821176033191584266293093888268802395595308944767408334671000721970171177 | PO_9131475498815360747307144 | {"materials-project-id": "mp-1066523", "hash": "3822345200460885929033732018831922989850783407511352836251855659212757348074927520673720814350514086619187073101426284816541128616508798286383136872140662", "id": "MD_3822345200460885929033732"} | MD_3822345200460885929033732 | null | [
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| 2023-12-01T22:55:06 | 4665790427388836302330955244750040986524666733890191252049693427566457597251105551843866638793585319792863747731828811276752113482625371379506874125171789 | CO_4665790427388836302330955 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -82.387245 | null | 1.389714 | 2.521963 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:23:57 | 11791611883673692256809407224690776189150281249725578156962162023987851631313218561059288834288414677052800640146906815765776288589303345844793589231248492 | PO_1179161188367369225680940 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 2414513046061416837464995714218224003648411891706001948058790827716087703057428335818533074557985463757069737190698779862742162741111200334099920366569399 | CO_2414513046061416837464995 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -68.348724 | null | 0.87636 | 1.561208 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:10:33 | 11767488240950252566870201250489220403008928902593180778882517189826304037446071441254154773460450297496272794887028236870808557223541485813291866567594235 | PO_1176748824095025256687020 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 9722554599550439076914695316066578926202995407174593044894924772839483946236690204154410099105607919056197757315233623590554889630314160335266980711303752 | CO_9722554599550439076914695 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -79.481308 | null | 0.931529 | 1.384417 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:20:57 | 5197600939772731002134463619351994243521050689642630347649572573860338137857709876701816127134811954670539191576299993613391912406864530912591791163905154 | PO_5197600939772731002134463 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 2662910901063127805536150535785539858489011909667594563255846471811394748567759754770921574107123481188733655033619842912559883418661952676772148811028356 | CO_2662910901063127805536150 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -76.683434 | null | 2.959003 | 3.36514 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:42:05 | 10767382631609773323269043391137746742988509697572231510218022090927730969734920443448879299522964336065402179275564793147634150749271047905874818008824356 | PO_1076738263160977332326904 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 2964312264899576124908821929971423038718599819087418146874529366785363239638139835918174335259700503520573007541610294647866001627160330078840162793167994 | CO_2964312264899576124908821 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 3847644983424253906822650250787129201295256918889248226332760550544879438209123175043963758089206055683730794422159610473258357848334329282568641201542293 | CO_3847644983424253906822650 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 2023-12-01T22:55:06 | 10067030691494550198687996180967593697156141604463176634708414416401774488884327146202511040408033815209042040755916823646415429749843278809308789453479961 | CO_1006703069149455019868799 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 2023-12-01T22:55:06 | 6767637063230638875760815817002853035553507794840266257142216805154453004782090770021429217408430378420725604389253630828164810210977646298197752248947984 | CO_6767637063230638875760815 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 2023-12-01T22:55:06 | 7788439993596592514534595027302435953450718997276570741489362454677545067497589720071899511328744701867331548843200316475168643433181176110724464023032474 | CO_7788439993596592514534595 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| 2023-12-01T22:55:06 | 291775593194569570485616660810197529421718088802462998612021088193131548513453220631847622189097158191436083151420878436632845977458135087299148018437209 | CO_2917755931945695704856166 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -165.921844 | null | 3.00378 | 5.468852 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:43:43 | 1516693242796526599355929053824396073773252948488084750721623942940300740776079157984578379835910726954115825204244057476273445376640524260645242371859634 | PO_1516693242796526599355929 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 8637828526973767997351539743344509374849257660978249167313258339534459460042566429838259028602311369767748742401310144779116739615095187226042102641767095 | CO_8637828526973767997351539 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
Mo8 | Mo | A | [
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| 2023-12-01T22:55:06 | 5212895557562160015963214834777021978903408908002536488311364665251956682258098316049748158006238456632443589352579015816325360317732794739537557019132714 | CO_5212895557562160015963214 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 8243970641031283221838106806954160234868381164125686476815073207818066007599676999438005515381308088811077134119618575115882952224397146135723252600835362 | CO_8243970641031283221838106 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -79.260712 | null | 0.619091 | 0.863735 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:13:44 | 6942022977319079081255444281533779154124302316461741281734323291350713031803857477217507972429756488132813534733202865067699797587305434272094432869687493 | PO_6942022977319079081255444 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 13163287518794874492151730685501190786891014954949832641303739784051528371923052955047823385445388462424340306387664266019800370616604848680595226428911651 | CO_1316328751879487449215173 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 7896850669672339718710514513725374458519185669048652352182650620384057895686545440025818500851457131838259430045359727708853471238921010154439016062975367 | CO_7896850669672339718710514 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -70.347023 | null | 3.098088 | 4.915487 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:36:09 | 12724433593688725234703532048718911303361385170449007592316132981820214015402966453851937551127041839668375555082409399228149740250327552842831447340101031 | PO_1272443359368872523470353 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 2600856150582782485101032197223637893242600518126013431848793428631380837372455417596919336147667838537303019729850676272982908036383994492761457286038084 | CO_2600856150582782485101032 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -171.844879 | null | 0.663415 | 1.03383 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:26:12 | 4303606558361407179188459616518846443865861529147356372398838121456035850299395842949219689491416209193663591773958344743450192459454794945868390448886876 | PO_4303606558361407179188459 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 9757646037196325140372085278074374690817339418814151936149472804983666457432955553294786079433618985850259751752511172790648189487011089541099613759821093 | CO_9757646037196325140372085 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 8282412397787791018493949852482329278090463659414700593822926117818119209686964641543389987294295601598407289264340255899079474868283967708399205547689836 | CO_8282412397787791018493949 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 3039790987708756324938165962103075885100675065842466591250467275508432407455917716541430238455594167927451395827202927899085778644741153108965560665730023 | CO_3039790987708756324938165 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -83.39415 | null | 0.623213 | 1.125783 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:00:56 | 12913503533072968655119682693313276374047499440196883305924626789149814859959415143802473472104811581200135678871096446479472538705383178546869619630221154 | PO_1291350353307296865511968 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 7144603634676129111239200517133147908427719746287702623065557503972009606355155677997830756583098390992350556514841433681953982739453318907707147286414709 | CO_7144603634676129111239200 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -75.053581 | null | 2.185136 | 3.00049 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:32:22 | 8891389818774240238046550689952563500989615630205132432975268760716373896369152601999040092691325554766719670290829147245629116279205832632695814339427471 | PO_8891389818774240238046550 | {"temperature": 2000, "hash": "5038272108731326564792169255289409101806296861397970431569880381208879553004280715011540754918172690659147781535715733200062875503570190465144527282590642", "id": "MD_5038272108731326564792169"} | MD_5038272108731326564792169 | null | [
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| [
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| 2023-12-01T22:55:06 | 12905462842879613584202291604834523005731174182745473500288249612387041557049336012110051297640699433654092634235170779795463536177617837610875327635004148 | CO_1290546284287961358420229 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
Mo8 | Mo | A | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -155.203415 | null | 1.528608 | 2.967995 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:35:43 | 5260365256584090053397248826977766570898359017702272203721932174755482010781378219144504739281532488448207234381700528111225053335695126638365801799304921 | PO_5260365256584090053397248 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -165.290268 | null | 3.211236 | 5.595025 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:13:01 | 10101171292812139968594166285722297688486508037787516954040209778467202016549867808007605912455672796094093397616415663167216562364128711587920647233107948 | PO_1010117129281213996859416 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 11747168257713551806611757566034553625857284644815409758800918269992618471670158216845637146127927516656878001430532635508926286091857532981441597610088013 | CO_1174716825771355180661175 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 5531676056003905695563915911674875394431633558455865304088025730314931073566594325526985416535480631654061744310047693634992197357971022214979585198691905 | CO_5531676056003905695563915 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 10372410146066433119516856273321423365971064700713608730648652267651496308521146779600237299807711449335659716963552152616537428954265548216882245724312826 | CO_1037241014606643311951685 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -79.255112 | null | 0.664364 | 0.742533 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:29:43 | 7392784723533669309310425935287011732314970632009608409745628433585347501424267088355948987364699228366910392010029086391726570011200595295140996431937268 | PO_7392784723533669309310425 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 30430740784502484420131123654064854574051575741795952827679506139622360806165009358680000547031470225813787115068992829628889219392933118717160031713394 | CO_3043074078450248442013112 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -79.261833 | null | 0.608802 | 0.877448 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:38:35 | 7250136226560721981199544679336233077246418692055912861827542122261076554845815906002044491287395302001923571763098464265643487444837720312346643961345730 | PO_7250136226560721981199544 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 11996239122361560473971657179073177628792951696645577983429268746132039476947735570224022078357072518052847739714295083532013826414719941622325231305212953 | CO_1199623912236156047397165 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -172.020203 | null | 0.454031 | 0.96084 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:05:40 | 3561961602536072594751262716237026519818583050483894801105868859950979145611576570228328912551922439730177669911597655486270861540278671857534849186457224 | PO_3561961602536072594751262 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 10214097381034089812698634700712990640318491702094745590891637208718153921563624393718421426673532438802033579853265655015304303496638501578200036781858092 | CO_1021409738103408981269863 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 2155058953760703855090189238519365586225208387223005534287756505014881970461022339876904877496021737048056865951913812171128345082797570935901243933091972 | CO_2155058953760703855090189 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -10.325183 | null | 61.513484 | 61.803836 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:28:32 | 10453262534616274018558749252328376517937739375118172449946789067314348720767758049715545167017365794550621448480063845959135612824193437905736228029893007 | PO_1045326253461627401855874 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 9433229249658894727471939274910297117961938227322521331447237044438588623671723783057939072240254561499727987047352426031377719217705755346016638500016461 | CO_9433229249658894727471939 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| true | null | null | -171.942291 | null | 0.628861 | 1.058906 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:12:17 | 613812498973813180044449288499041160252346358568489151886889406647053641746455903011675116185692970345715884243622362646755006016458639632883940991639795 | PO_6138124989738131800444492 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 4109109502795001625405857227211187104859130067318424347209632095466367155392783179041065559045020560991536198884286335917510544916353600513595523876736106 | CO_4109109502795001625405857 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -81.944794 | null | 1.878216 | 3.005734 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:17:25 | 2406500857983310844531004251963350918967342607639353550310586830656953341563885968242953445544976342909165889614006546355971691054066185145204557169131513 | PO_2406500857983310844531004 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 3412582816808279756882920333500137088529316466500181636894844761430915338517120308993115170719056209736033422361189577689556617245082452552344441716275442 | CO_3412582816808279756882920 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -155.299179 | null | 1.458871 | 2.283727 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:18:52 | 7393569958216824060805253788389265052636558362241171124393542297657270636913009046642175663900356021063111297069062211249209386594151151222794176349107956 | PO_7393569958216824060805253 | {"materials-project-id": "mp-129", "temperature": 2000, "hash": "2752402640170085491037779625583173432485640205549257074432955249789210333243706007248538934342899300701005104319381149233012168703666164255726158527965535", "id": "MD_2752402640170085491037779"} | MD_2752402640170085491037779 | null | [
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| 2023-12-01T22:55:06 | 6882260355561133026232636909110324810079672280237871844581195732269691361030840804416860840130260602392708292233795363024604728412238674034491219767240287 | CO_6882260355561133026232636 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -79.3395 | null | 0.650649 | 1.208366 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:02:58 | 1272279682877002904499931768171222083771425030025544432695284040728961304022423618496293883626108044372059645537688628800617735981728519567365181534364758 | PO_1272279682877002904499931 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 1173912294740939425487224283246999115578020481407126548093616175522367743201400000268210791429890420264921019969678781540565681638398116463601667715786131 | CO_1173912294740939425487224 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -69.535072 | null | 3.437409 | 4.941896 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:16:54 | 12828644598652753707198687797172618216573721214280524954434460709361881780433469076635475462010191956597330799295917852009578532597165532210114742075232227 | PO_1282864459865275370719868 | {"materials-project-id": "mp-1056004", "temperature": 2000, "hash": "10461696663612406624453983347386637607736507568896207313294354666817255449357104525684871717083965240123217027519409833598539587234038925429349282484765438", "id": "MD_1046169666361240662445398"} | MD_1046169666361240662445398 | null | [
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| 2023-12-01T22:55:06 | 4711314508669750066346142267516175317697331493712070212651067666746131698996343631401275091123323587457405039124001134587020238247125108398982492865935672 | CO_4711314508669750066346142 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -83.310753 | null | 1.480134 | 2.974308 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:27:57 | 3808747953102285366970677312216522654952823374978000439117712772043201162860688391910496526854747347898185306667434395545284620156661355621446668383589587 | PO_3808747953102285366970677 | {"materials-project-id": "mp-1056004", "temperature": 2000, "hash": "10461696663612406624453983347386637607736507568896207313294354666817255449357104525684871717083965240123217027519409833598539587234038925429349282484765438", "id": "MD_1046169666361240662445398"} | MD_1046169666361240662445398 | null | [
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| [
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| 2023-12-01T22:55:06 | 11018684114487247024690874926670777188465617997942699341530761835886369767656169557668412003268561014339837865767541877939947226841445475449358394996909368 | CO_1101868411448724702469087 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 2023-12-01T22:55:06 | 11636410220002832788086346573899200168660079147993223468708166331613619211555718888804869924517066645301350228758711173477205845958246792119179777513198508 | CO_1163641022000283278808634 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -156.891266 | null | 1.367038 | 3.035811 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:57:21 | 6804342258995372164162326604414105803259190950960999443018497376905497945235703990827201671563205547860221031208803283412374202489024627498659026925503871 | PO_6804342258995372164162326 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 1235363491002926040735271104706795279323410008772462425671516197666766775895329117287711161449507121058849006199303413885889535492804730608687495707990936 | CO_1235363491002926040735271 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -107.185783 | null | 4.989771 | 8.964172 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:51:08 | 810215159650325547793649946101643470942544060773867898233409549658737923961842261777522600770181817189313612586770170988088875946820699897003608974569787 | PO_8102151596503255477936499 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 12887876761838661720935928977132929457301062525667075413725188172157530459613990470157046451109247629562540362058773376997166869937580549854602329523295868 | CO_1288787676183866172093592 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
Mo32 | Mo | A | [
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| 2023-12-01T22:55:06 | 2964943991441323008433594077302263314917510610347082485621741608480874934343040314964338054038127567757283254331339782785698007956891775781952906474776741 | CO_2964943991441323008433594 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 6913964844889257233532351629646270241114655808242613964182571327235853036851819798524852507547020056377121432788837316659668630334768140811289253971083488 | CO_6913964844889257233532351 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -76.62471 | null | 1.034578 | 1.637383 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:15:48 | 592676216115199769919101408983919342942006265383863136393379534248875758322410748540618144191948429326537678406825776806147437300784643824097755437545869 | PO_5926762161151997699191014 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 5702991370499300203605300944490092628224090274263079341745702024534373286389344594836622554052125740247932810664751255511309926897546805488565072137205923 | CO_5702991370499300203605300 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -74.950012 | null | 0.452887 | 0.739834 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:43:41 | 1459347869193257507448378125399708901850952849451251933764701724221126666622332514973301523195045672072304595364361993054951586660574520593630796704944590 | PO_1459347869193257507448378 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| [
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| 2023-12-01T22:55:06 | 8188864290394943763558229301458020609713179796067988620702168980496282620576133845172582224324176923074288930448458152430185950295940072041020215950152951 | CO_8188864290394943763558229 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
"Christopher M. Andolina",
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]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -167.272247 | null | 2.109074 | 3.951167 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:38:24 | 582656161512903665045916465812687517432220360999362975743454309652149458237000126990005912197215312719771989030522193190985948740643232615801702559154711 | PO_5826561615129036650459164 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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]
| [
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| 2023-12-01T22:55:06 | 2018515571531570894628995859113522926184825886860061897962058636174862401630213747442517835019567553627089091434918743885640969090105715176285169647271442 | CO_2018515571531570894628995 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
"Christopher M. Andolina",
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]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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1
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -82.944679 | null | 1.250954 | 1.92744 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:20:15 | 11380580532714037305988983952345333400704698975235055434282992406119056550941148666209405330490183795401833661738711653623114992513094448368798311242183782 | PO_1138058053271403730598898 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 792231175386692445939787717355688584703476041077078895971169764350174320662592179013908312298059086386577083036639658875388626334163487952967562678826393 | CO_7922311753866924459397877 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -160.287064 | null | 1.03097 | 1.983537 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:33:32 | 12803747090032511562544099299294638453352467504925292960112833779771845105081527795953628861024883041124319123909066973877086751814578896047747608692520508 | PO_1280374709003251156254409 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 13109326126120482768534246716867994466548551934822709426147172463148580951167387640705859445170194143750464979486408484162685576067219128112396338488237111 | CO_1310932612612048276853424 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -166.240128 | null | 1.932754 | 3.452785 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:23:32 | 4253656937448816588768735423286382141733568272959227113908862253962228260163700752438768207895450269656439679050369419732289504801481332042952438240105887 | PO_4253656937448816588768735 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -160.945297 | null | 0.808802 | 1.596632 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:49:36 | 4853556538767735451271205708024391411649524915753082746827195222597648774924972252334373571089657951234854501747248272960123717697477571755705550866721750 | PO_4853556538767735451271205 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 2875354445219957466695366268150300433758387277105252566650595110581765268674681047360930938093462956325380538351250934432064500791902051551526825601767655 | CO_2875354445219957466695366 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -74.739136 | null | 2.177818 | 3.433366 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:17:29 | 7119457662864932770568736050476575516696879479896460119873601137298460365531347271050846850876489645536149559311755681364239557693871410660839761695730806 | PO_7119457662864932770568736 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 12779531498316019235528051152939907032732523853886772992099483853658986074733945712668950371498135166165910610061358468423938044580530260797075808512452358 | CO_1277953149831601923552805 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -166.292023 | null | 2.415884 | 4.872167 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:55:05 | 12538687619875778749571801750470297096695788987371004165183003994395130070155106883800722488520502065307316029242835887256907840372993227214519599386754484 | PO_1253868761987577874957180 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 13103625112535748109489811918373920212944918302919209721435168321797805640589669464746366966262284737680705339766062352162170438192592429202051768140392691 | CO_1310362511253574810948981 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 2445830085130278960293293574707681860852873879172048028583398189265353818481604385943084507346674354938882808415772678646573941478532727710429993310289126 | CO_2445830085130278960293293 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -69.689659 | null | 0.863023 | 1.429761 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:31:06 | 3687320370911216622875296531415507897584154097373822730930838007255645703144016894167510643164413067685266813688307448248705580637152186312895335272407791 | PO_3687320370911216622875296 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 1915326819748746474142236246182052213951093290742815168859481721998944026051306714278366876629323645096281614555645679135216418307201351673342946959484259 | CO_1915326819748746474142236 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -77.005295 | null | 0.758919 | 1.299165 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:41:18 | 8509283516699197675673775683491612984436371713715220894304698733249383387426031855435875139945140628445858591709956360472913822662194234155178438564666330 | PO_8509283516699197675673775 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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]
| [
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| 2023-12-01T22:55:06 | 9897979041908993332523052483080295911435644552921272599883560763120112483735667736061487894555028479998292895060984280865915668151513309018014863158861605 | CO_9897979041908993332523052 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
"Christopher M. Andolina",
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]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -82.047607 | null | 0.924097 | 1.444082 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:51:14 | 5936319925433564522239761333025592825498695068160698111481254280410888422201739078457777462065662227676297983924190887081084933996507924312139825495347978 | PO_5936319925433564522239761 | {"temperature": 2000, "hash": "5038272108731326564792169255289409101806296861397970431569880381208879553004280715011540754918172690659147781535715733200062875503570190465144527282590642", "id": "MD_5038272108731326564792169"} | MD_5038272108731326564792169 | null | [
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| 2023-12-01T22:55:06 | 12653683148501384810289326570511969446391116184271574493887660554525423570436671024624681965364564519890877132276129721463651567212601805859611311519804900 | CO_1265368314850138481028932 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -76.363037 | null | 0.76506 | 1.291253 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:42:49 | 6319534964599163376377738580441402891204522328831714295210054039868182895370515653194249466217033878032006084803125828120026737904973623791889188511011195 | PO_6319534964599163376377738 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 4396720834780347394340722703982741207982833036622405482228016700543983540729060393154123009544792584123554407367022547715573996016025894426207364406568762 | CO_4396720834780347394340722 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -75.443321 | null | 0.862637 | 1.39635 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:26:42 | 2069767746866600068705129249673287196277737630020669800336120815211523501429740397676234322028165759097225326776774518760637061216466175871677037726809796 | PO_2069767746866600068705129 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| 2023-12-01T22:55:06 | 9538529344793246665284227178986949627215786662908274298156288098369324633939076953146480427553059002419537132950822914908322560289727631199019599366668213 | CO_9538529344793246665284227 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| 2023-12-01T22:55:06 | 5819498304312615601079439155097786769946942915086284150705332186277468691715296574380683627803808144749100385821677425399715436810473461395192105926057262 | CO_5819498304312615601079439 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -83.369255 | null | 0.775539 | 1.706363 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:44:39 | 2617078287559821594822648633296911534499993396040876986131827937308226541334647913550487563724052295015082300366665563801230992409809257625583697442400709 | PO_2617078287559821594822648 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 9084023547284920391157166307444354170210340281746830754397910674562227276088895728283358417648823325682906450738158839288122776947040705112941662323235067 | CO_9084023547284920391157166 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| true | null | null | -132.039551 | null | 3.101494 | 5.486943 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:39:01 | 13260101166548804333131929425534945220380780396257523154372139886488633167607681540900900196161944980337315000330803320987098360436377113999700789453081288 | PO_1326010116654880433313192 | {"materials-project-id": "mp-129", "temperature": 2000, "hash": "2752402640170085491037779625583173432485640205549257074432955249789210333243706007248538934342899300701005104319381149233012168703666164255726158527965535", "id": "MD_2752402640170085491037779"} | MD_2752402640170085491037779 | null | [
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| 2023-12-01T22:55:06 | 2445989899109289345197739153357263054195472848091118301292665319538636943234037094268228517204672941317576661422701562405018222862313920605533237570009229 | CO_2445989899109289345197739 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| true | null | null | -329.933044 | null | 1.156274 | 2.155551 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:31:04 | 3528438024963077032724784381805146047723693432458971458621555238410292575723049843427202363035057138184773814325446514356174496908761641731649177720851870 | PO_3528438024963077032724784 | {"materials-project-id": "mp-8637", "hash": "6966233607090115772682513866328493864257802604788524296728866462601494288840305587996997246366084672544881309975060598897334822454883723648657276032455554", "id": "MD_6966233607090115772682513"} | MD_6966233607090115772682513 | null | [
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| 2023-12-01T22:55:06 | 10684992641668702230087972189690365439312936902181882763655848093042004830426262408966434120677813959337375219174197145913670474334824323693711244689835472 | CO_1068499264166870223008797 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -328.668457 | null | 1.170206 | 1.952563 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:59:31 | 13190550287234413305127697064311825232061776558491929851448083575045793626276182041578102861821755975403834588727279836118328779720374895916237865765342496 | PO_1319055028723441330512769 | {"materials-project-id": "mp-8637", "hash": "6966233607090115772682513866328493864257802604788524296728866462601494288840305587996997246366084672544881309975060598897334822454883723648657276032455554", "id": "MD_6966233607090115772682513"} | MD_6966233607090115772682513 | null | [
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| 2023-12-01T22:55:06 | 1404533257257540263369114731851961515187043400252878011209232895573457533422716328196323738315907567239057201409119826372270068291158879179367914952664698 | CO_1404533257257540263369114 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -76.690971 | null | 0.902542 | 1.219809 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:45:01 | 4477310656297570947269021448128187832274378482680477380037899696887714658023933191583468472794054525661127045040676339830016034414652110070986906197036049 | PO_4477310656297570947269021 | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} | MD_1086845055867124744315202 | null | [
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| 2023-12-01T22:55:06 | 6685175088659215990155331591390734461773732293545736202686298405933147766283504554556669283524186919799000311035636916031961388313429056731698396407059715 | CO_6685175088659215990155331 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -166.958771 | null | 2.11217 | 3.736591 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:44:09 | 9206552568829002887219665534786905929124345882524287306696690573781101816719915998496796599624949889106210768163415612941135466305019050941697479405187783 | PO_9206552568829002887219665 | {"materials-project-id": "mp-129", "temperature": 2000, "hash": "2752402640170085491037779625583173432485640205549257074432955249789210333243706007248538934342899300701005104319381149233012168703666164255726158527965535", "id": "MD_2752402640170085491037779"} | MD_2752402640170085491037779 | null | [
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| [
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| 2023-12-01T22:55:06 | 4461328063033882865573308727330774047374456389460405584714808904126657822483912241036268355180207233173893653389340982745779911550541173342648843471845925 | CO_4461328063033882865573308 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -320.406921 | null | 1.678212 | 4.142364 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:41:10 | 10418615801688644783422125639947638139161981936174082698652784532871956253393186547966729964172038767998287093685244238088059572474740811733669692907122006 | PO_1041861580168864478342212 | {"materials-project-id": "mp-1066523", "hash": "3822345200460885929033732018831922989850783407511352836251855659212757348074927520673720814350514086619187073101426284816541128616508798286383136872140662", "id": "MD_3822345200460885929033732"} | MD_3822345200460885929033732 | null | [
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| 2023-12-01T22:55:06 | 8701411322826906737635143806298757727762698620700527674844970168445235801999376985419414034745874102715582050637273688078375886080833744544725666645969716 | CO_8701411322826906737635143 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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| true | null | null | -70.103714 | null | 0.867424 | 1.41701 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:23:00 | 5662488986769791397697884075829765104560218533362155273429884829893947022971047863114894628143230398535687586364806379571374060452043847046643487573229014 | PO_5662488986769791397697884 | {"materials-project-id": "mp-1056004", "hash": "1546279540348808226191426280339017982600682782567448803308393499565995436710989199120281085154028338732977293131397308549833343550702523976283983782478882", "id": "MD_1546279540348808226191426"} | MD_1546279540348808226191426 | null | [
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| [
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| 2023-12-01T22:55:06 | 8625392740487308808151053005411127365337974019155183735753753928588985771922526979529002893300210276129049386885738260283446832277302600745370610853916534 | CO_8625392740487308808151053 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
"Christopher M. Andolina",
"Wissam A. Saidi"
]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
Mo17 | Mo | A | [
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| [
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| 1 | 17 | [
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| true | null | null | -105.459953 | null | 4.363263 | 9.88568 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:40:42 | 1581753103510131977160270115694293682382513278065082485673941246937360923589394823546389878508465401373890782610557106283709855081292416459893015381041933 | PO_1581753103510131977160270 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| [
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| 2023-12-01T22:55:06 | 3576287999219202872776549283021347875620650579956724253792934235336364380276567320514853031959176745940204255966642740539237339419850454535930855113450240 | CO_3576287999219202872776549 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
"Christopher M. Andolina",
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]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
Mo32 | Mo | A | [
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| 2023-12-01T22:55:06 | 1684867012454035445413368071615981513526998323133171012421955167092044230360965307508008546072670339491609904641964129726131755384674110070772254356042396 | CO_1684867012454035445413368 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -167.973419 | null | 2.056924 | 4.038033 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:12:32 | 5269613629976154466579348596435547947938015632003758822087498281434200475564521101559605578580803016525966132805455434643828778385621730966331802553727246 | PO_5269613629976154466579348 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 12954281469367340047883278144260209901221319957929136120818537128269718525988252432031852360933422271567135240346293705644301555413328146453769432590821268 | CO_1295428146936734004788327 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -125.602859 | null | 3.754899 | 7.279766 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:22:14 | 12006963763332900553032330230688947840618927564850718849629506133730388366633919726822710495133132692308950796592962872060954035811908280670553520099544151 | PO_1200696376333290055303233 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
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| 2023-12-01T22:55:06 | 11986907298626983040602072997337083075023348497669009848619550964665952565131118356001460097479734492429512737813297572610696013034803681789641554297131504 | CO_1198690729862698304060207 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| 2023-12-01T22:55:06 | 12134386908519852055090984021496597523767342147766480936017780305879858141266998178611888772247032855222173547521866865926250364067721918373789531334836754 | CO_1213438690851985205509098 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
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| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
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| true | null | null | -312.101257 | null | 1.311774 | 3.56682 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T15:05:42 | 3769996992467988403081015677789513682091075759358065949465790511647828682852660767142705653044496742802989686548244469815388748092637838360461321570339485 | PO_3769996992467988403081015 | {"materials-project-id": "mp-1066523", "hash": "3822345200460885929033732018831922989850783407511352836251855659212757348074927520673720814350514086619187073101426284816541128616508798286383136872140662", "id": "MD_3822345200460885929033732"} | MD_3822345200460885929033732 | null | [
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]
| [
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| 2023-12-01T22:55:06 | 2966198140279929737716341390085984571910244323794851306173299958087758986219109462905645567905321027256724176855985931059365822319218899908195916898124047 | CO_2966198140279929737716341 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
"Christopher M. Andolina",
"Wissam A. Saidi"
]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
"Mo"
]
| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
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| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
Mo16 | Mo | A | [
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| [
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| 1 | 16 | [
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| 3 | 10617502270103580742415121867608905796830405598104289470037796239883225257181891181677932403535261886633944103554899104940141880377240540245361978516731490 | 1 | VASP | DFT-PBE | null | [
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| null | [
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| true | null | null | -166.285553 | null | 2.34266 | 4.01337 | {"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"} | MD_1213582242930949697188121 | 2024-08-16T14:24:51 | 8556858536156350267815518099188694164847423151896282813851320527120221667655537625747995972325504568125587887257693332921922154646753255026728571455295656 | PO_8556858536156350267815518 | {"materials-project-id": "mp-129", "hash": "1218455025909496628366994563810761640983475988122450511817027697629645869862679470705626794401676601487379024753909861487688483579254366693491109188644419", "id": "MD_1218455025909496628366994"} | MD_1218455025909496628366994 | null | [
"Mo_iter_all_Mo_iter1_0_mp-129_elastic_B222_dist03_10_6"
]
| [
"DS_bjkftn0ug9r3_0"
]
| 2023-12-01T22:55:06 | 944447496065347358257672864058156569547344231512890026685458302042701990367814325867362590011299056343446036866424621711916168231571813432114426603901971 | CO_9444474960653473582576728 | 23-Single-Element-DNPs_RSCDD_2023-Mo | [
"Christopher M. Andolina",
"Wissam A. Saidi"
]
| Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K. | [
"Mo"
]
| 1 | 3,718 | 3,718 | 66,612 | 0 | 3,718 | 0 | 3,718 | 0 | 3,718 | -164.280905 | 10,531.926849 | 0 | 2023-12-01T17:55:15 | [
[
1,
1,
1
]
]
| [
3
]
| 2023 | [
1
]
| GPL-3.0-only | {'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None} | 10.60732/b6ece7fd | 4341916650885590357937254568610900649722899683832719330248013647339970982516281455657803549901939464595696190490245681971031959657284286710420285902088266 | DS_bjkftn0ug9r3_0 | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
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