Dataset Viewer
chemical_formula_hill
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sequence | elements_ratios
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bool | electronic_band_gap
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string | energy
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float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
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sequence | configuration_names
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string | dataset_name
string | dataset_authors
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| true | null | null | 287.693848 | null | 14.783889 | 30.42057 | {"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 | 2024-08-16T14:26:58 | 12872827634589640466251412200599429565989002987195014676668023212645290855656310391834450348685543724857975174860552107650074301727778413768529074215235918 | PO_1287282763458964046625141 | {"materials-project-id": "mp-23153", "temperature": 105, "hash": "5667758091971450495693170744432283119764234280044477759801932042943588727479069638107904638582965496369467634446877084093007078093448477443206969893418484", "id": "MD_5667758091971450495693170"} | MD_5667758091971450495693170 | null | [
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| [
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| 2023-12-01T22:54:19 | 10013322697167042822993061945027076503898290274915404903137653085360209062634579387925501152911572390310740361999860323179883482230367217815150349579200396 | CO_1001332269716704282299306 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| Configurations of I 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 | 4,532 | 4,532 | 115,902 | 0 | 4,532 | 0 | 4,532 | 0 | 4,532 | -5.655005 | 9,593.550587 | 0 | 2023-12-01T17:54:36 | [
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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} | 10.60732/57c54149 | 1483178602357787476456916624377487710962191162257948474794705061528058955454962820352002043088002119793270628374932875338986658690304418622288995738492297 | DS_gc1y80tpyylb_0 | 23-Single-Element-DNPs_RSCDD_2023-I__Andolina-Saidi__DS_gc1y80tpyylb_0 |
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| 2023-12-01T22:54:19 | 10172089948621068567608319513891407952440834050845201785573307927543524712310033592665728976552971841512621167661982999089789091884059914520557296299614736 | CO_1017208994862106856760831 | 23-Single-Element-DNPs_RSCDD_2023-I | [
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| Configurations of I 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 | 4,532 | 4,532 | 115,902 | 0 | 4,532 | 0 | 4,532 | 0 | 4,532 | -5.655005 | 9,593.550587 | 0 | 2023-12-01T17:54:36 | [
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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} | 10.60732/57c54149 | 1483178602357787476456916624377487710962191162257948474794705061528058955454962820352002043088002119793270628374932875338986658690304418622288995738492297 | DS_gc1y80tpyylb_0 | 23-Single-Element-DNPs_RSCDD_2023-I__Andolina-Saidi__DS_gc1y80tpyylb_0 |
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| Configurations of I 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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0.02278118021786213,
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0.030917389318346977,
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0.06483352929353714
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[
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[
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0.014663539826869965,
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[
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[
0.017150679603219032,
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0.1055653914809227,
0.02682960033416748,
0.2116941660642624
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[
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| null | [
[
0.2962599992752075,
0.01664300076663494,
0.8943979740142822
],
[
0.016635999083518982,
-0.3703089952468872,
0.003998999949544668
],
[
0.8943989872932434,
0.004000999964773655,
1.8938299417495728
]
]
| true | null | null | -96.874962 | null | 0.172799 | 0.423436 | {"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 | 2024-08-16T14:35:07 | 6909391340733406090790267792865708739290008629561639225989964277868365044310294311180915169209538344002625886508493979749949234525722124289508133666925068 | PO_6909391340733406090790267 | {"materials-project-id": "mp-23153", "temperature": 420, "hash": "3571143252367592943292734308767478741631058341465699072692346118474668104552841293857544432576409953248442026102484375624596587164967844387116473887586712", "id": "MD_3571143252367592943292734"} | MD_3571143252367592943292734 | null | [
"I_iter_all_I_iter0_420_0_mp-23153_elastic_B222_dist03_2_12"
]
| [
"DS_gc1y80tpyylb_0"
]
| 2023-12-01T22:54:19 | 10253170827265084547474583867651733498104981385241856289524539482007640486183772517816317254899604874719156479345171161401103661935805793655297164328021263 | CO_1025317082726508454747458 | 23-Single-Element-DNPs_RSCDD_2023-I | [
"Christopher M. Andolina",
"Wissam A. Saidi"
]
| Configurations of I 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. | [
"I"
]
| 1 | 4,532 | 4,532 | 115,902 | 0 | 4,532 | 0 | 4,532 | 0 | 4,532 | -5.655005 | 9,593.550587 | 0 | 2023-12-01T17:54:36 | [
[
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} | 10.60732/57c54149 | 1483178602357787476456916624377487710962191162257948474794705061528058955454962820352002043088002119793270628374932875338986658690304418622288995738492297 | DS_gc1y80tpyylb_0 | 23-Single-Element-DNPs_RSCDD_2023-I__Andolina-Saidi__DS_gc1y80tpyylb_0 |
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Dataset
23-Single-Element-DNPs RSCDD 2023-I
Description
Configurations of I 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.
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Christopher M. Andolina, Wissam A. Saidi
Publication
https://doi.org/10.1039/D3DD00046J
Original data link
https://github.com/saidigroup/23-Single-Element-DNPs
License
gpl-3.0
Number of unique molecular configurations
4532
Number of atoms
115902
Elements included
I
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Andolina, C. M., and Saidi, W. A. 23-Single-Element-DNPs RSCDD 2023-I. ColabFit, 2023. https://doi.org/10.60732/57c54149
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