chemical_formula_hill
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int32 | software
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float64 | electronic_band_gap_type
string | energy
float64 | formation_energy
float64 | max_force_norm
float64 | mean_force_norm
float64 | property_object_metadata
string | property_object_metadata_id
string | property_object_last_modified
timestamp[ns] | property_object_hash
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string | configuration_metadata
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list | configuration_dataset_ids
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string | dataset_authors
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string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cu32
|
Cu
|
A
|
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[
"Cu"
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[
1
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[
true,
true,
true
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[
1,
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| 1 |
VASP
|
DFT-PBE
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] | null |
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] | true | null | null | -109.393799 | null | 1.641583 | 4.369369 |
{"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
| 2024-08-16T15:06:56 |
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|
PO_7440042461049174446955514
|
{"materials-project-id": "mp-30", "temperature": 1400, "hash": "8495636077538216939536635379244474204165680900104957820640201532737276966007943778867825222405747515176057416471562302984749176270215765774074301963875404", "id": "MD_8495636077538216939536635"}
|
MD_8495636077538216939536635
| null |
[
"Cu_iter1_0_mp-30_elastic_B222_dist03_9_1400_4"
] |
[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:03 |
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|
CO_1226019380346875659110702
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
"Wissam A. Saidi"
] |
Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
[
[
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/e0a72dd8
|
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|
DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
|
Cu48
|
Cu
|
A
|
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[
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[
1
] | 1 | 48 |
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[
true,
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true
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[
1,
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1
] | 3 |
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| 1 |
VASP
|
DFT-PBE
| null |
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MD_1213582242930949697188121
| 2024-08-16T14:35:30 |
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PO_2886565080895014916180041
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MD_1930707603777017050011070
| null |
[
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[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:04 |
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CO_9638783610892411169326511
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
"Wissam A. Saidi"
] |
Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
[
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3
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2023
|
[
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/e0a72dd8
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|
DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu48
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Cu
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A
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VASP
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DFT-PBE
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{"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
| 2024-08-16T15:38:44 |
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PO_1275752544859647653340015
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{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
|
MD_1930707603777017050011070
| null |
[
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] |
[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:04 |
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|
CO_8605425856538703123618693
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
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Configurations of Cu 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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MD_1213582242930949697188121
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[
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Configurations of Cu 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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MD_1213582242930949697188121
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PO_4022822315349342381352663
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CO_1084320500655931372008935
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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[
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GPL-3.0-only
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu31
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DFT-PBE
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MD_1213582242930949697188121
| 2024-08-16T15:04:22 |
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PO_9436070657086754464788038
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MD_7876487181888821678431936
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[
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CO_7087061742913914155029329
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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MD_1213582242930949697188121
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[
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Configurations of Cu 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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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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VASP
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DFT-PBE
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MD_1213582242930949697188121
| 2024-08-16T14:52:49 |
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PO_5435758494118386465869056
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MD_8495636077538216939536635
| null |
[
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[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:03 |
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CO_1607580471858032841593234
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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"Wissam A. Saidi"
] |
Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
[
[
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1
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[
3
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2023
|
[
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/e0a72dd8
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|
DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
|
Cu48
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Cu
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A
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[
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1
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| 1 |
VASP
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DFT-PBE
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{"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
| 2024-08-16T15:40:15 |
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|
PO_5816212206742122473662071
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{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
|
MD_1930707603777017050011070
| null |
[
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] |
[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:03 |
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|
CO_3195725770333694704676236
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
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Configurations of Cu 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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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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[
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MD_1213582242930949697188121
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Configurations of Cu 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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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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Configurations of Cu 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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VASP
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DFT-PBE
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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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
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GPL-3.0-only
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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DFT-PBE
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MD_1213582242930949697188121
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MD_7876487181888821678431936
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[
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CO_6087416732556256625330406
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu48
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Cu
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A
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MD_1213582242930949697188121
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[
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[
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CO_1121351316802579813841177
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
[
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3
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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}
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10.60732/e0a72dd8
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DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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A
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MD_1213582242930949697188121
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Configurations of Cu 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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MD_1213582242930949697188121
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MD_8629312491804937715709257
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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MD_1213582242930949697188121
| 2024-08-16T15:13:55 |
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PO_9134665368511135507128374
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[
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CO_5757234974399434317955533
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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
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GPL-3.0-only
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10.60732/e0a72dd8
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu48
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MD_1213582242930949697188121
| 2024-08-16T14:27:44 |
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[
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[
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CO_4731079313317715931111144
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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"Wissam A. Saidi"
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Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
[
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2023
|
[
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GPL-3.0-only
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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10.60732/e0a72dd8
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|
DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu33
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Cu
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A
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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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MD_1213582242930949697188121
| 2024-08-16T14:44:29 |
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PO_2215124804387765179384711
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|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu31
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Cu
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MD_1213582242930949697188121
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CO_6761314791832610917245194
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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[
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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MD_1213582242930949697188121
| 2024-08-16T14:35:50 |
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PO_7797351536626160468830044
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MD_9528764851809222476042710
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[
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CO_2662002420582954954129842
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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Cu
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MD_1213582242930949697188121
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PO_3254662034024619776065745
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MD_9023465436536084198450125
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[
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CO_8479069102921274313981406
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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.
|
[
"Cu"
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2023
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GPL-3.0-only
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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10.60732/e0a72dd8
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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A
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MD_1213582242930949697188121
| 2024-08-16T14:48:54 |
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PO_4462180888713532266864499
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MD_1001864881812158934307091
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[
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CO_1209809975313737425148043
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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.
|
[
"Cu"
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2023
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GPL-3.0-only
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
|
Cu16
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Cu
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A
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MD_1213582242930949697188121
| 2024-08-16T14:29:14 |
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PO_9817368810598724767937979
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MD_5096852878689515016501519
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CO_8139443478040700594919782
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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.
|
[
"Cu"
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2023
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GPL-3.0-only
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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Cu
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A
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MD_1213582242930949697188121
| 2024-08-16T15:33:28 |
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PO_1119575369400781666687808
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MD_8629312491804937715709257
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[
"DS_dc3o40aou2le_0"
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CO_7723079716398614423492032
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu48
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Cu
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[
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[
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DFT-PBE
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MD_1213582242930949697188121
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MD_1930707603777017050011070
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CO_1004265850256118170077204
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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[
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10.60732/e0a72dd8
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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MD_1213582242930949697188121
| 2024-08-16T14:32:28 |
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PO_3503110979273376733519359
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MD_9528764851809222476042710
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[
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CO_7585766649059469275915918
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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Cu
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A
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DFT-PBE
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MD_1213582242930949697188121
| 2024-08-16T15:09:42 |
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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[
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Cu16
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Cu
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MD_1213582242930949697188121
| 2024-08-16T14:25:49 |
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MD_9023465436536084198450125
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[
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CO_5008903154806304079216666
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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MD_1213582242930949697188121
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PO_1028229313426179193660339
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[
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CO_5311175349137608583274924
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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"Wissam A. Saidi"
] |
Configurations of Cu 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.
|
[
"Cu"
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2023
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GPL-3.0-only
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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10.60732/e0a72dd8
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu33
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Cu
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A
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MD_1213582242930949697188121
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PO_1044578577313968552331536
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[
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CO_1144554667431462139685378
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
"Wissam A. Saidi"
] |
Configurations of Cu 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.
|
[
"Cu"
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3
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2023
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GPL-3.0-only
|
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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MD_1213582242930949697188121
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[
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CO_1081279964980397020294636
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
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Configurations of Cu 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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MD_1213582242930949697188121
| 2024-08-16T15:26:58 |
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23-Single-Element-DNPs_RSCDD_2023-Cu
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Configurations of Cu 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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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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MD_1213582242930949697188121
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CO_4022499239191531247199177
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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MD_1213582242930949697188121
| 2024-08-16T15:30:43 |
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PO_6914330798994377495696761
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MD_9023465436536084198450125
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[
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CO_6523473565282199372438721
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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MD_1213582242930949697188121
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[
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Configurations of Cu 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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[
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Cu16
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MD_1213582242930949697188121
| 2024-08-16T14:30:52 |
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PO_1245358233087780440925548
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MD_9023465436536084198450125
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[
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CO_7039876863271657392384417
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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
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GPL-3.0-only
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu32
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Cu
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MD_1213582242930949697188121
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Configurations of Cu 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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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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MD_1213582242930949697188121
| 2024-08-16T15:09:59 |
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[
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CO_4107955137727781575777246
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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
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Cu16
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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu33
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MD_1213582242930949697188121
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PO_1240175702985290878344688
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[
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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MD_1213582242930949697188121
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Configurations of Cu 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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MD_1213582242930949697188121
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MD_8629312491804937715709257
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CO_2126159518809280174693974
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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[
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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MD_1213582242930949697188121
| 2024-08-16T15:10:54 |
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PO_6770233346608509088187138
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MD_7900197416090488123862896
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[
"DS_dc3o40aou2le_0"
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CO_1107954327670531087335148
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
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Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
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2023
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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MD_1213582242930949697188121
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MD_9023465436536084198450125
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[
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CO_4784290178148047212552563
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu33
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Cu
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|
MD_1213582242930949697188121
| 2024-08-16T14:24:34 |
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PO_2245251511819495797633791
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MD_7876487181888821678431936
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[
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|
CO_2999390223704570425980390
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
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] |
Configurations of Cu 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.
|
[
"Cu"
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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/e0a72dd8
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|
DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
|
Cu16
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Cu
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A
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MD_1213582242930949697188121
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Configurations of Cu 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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MD_1213582242930949697188121
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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MD_1213582242930949697188121
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PO_8983184699213674222556426
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MD_2235029950651867963297679
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[
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CO_5732495953534666885290739
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23-Single-Element-DNPs_RSCDD_2023-Cu
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[
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Configurations of Cu 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.
|
[
"Cu"
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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Cu
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MD_1213582242930949697188121
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MD_9023465436536084198450125
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[
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[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:04 |
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CO_3513517040556079145417591
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
"Wissam A. Saidi"
] |
Configurations of Cu 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
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu48
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Cu
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MD_1213582242930949697188121
| 2024-08-16T15:36:16 |
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MD_1930707603777017050011070
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CO_6415438464049392399780755
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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.
|
[
"Cu"
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2023
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[
1
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GPL-3.0-only
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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10.60732/e0a72dd8
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu31
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Cu
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A
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[
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MD_1213582242930949697188121
| 2024-08-16T15:31:00 |
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PO_5722534633476232420591433
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MD_8495636077538216939536635
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[
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CO_8014910613792848212034894
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
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Configurations of Cu 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.
|
[
"Cu"
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2023
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GPL-3.0-only
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DS_dc3o40aou2le_0
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu16
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Cu
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DFT-PBE
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MD_1213582242930949697188121
| 2024-08-16T15:18:45 |
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PO_1066100765098735105979056
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{"materials-project-id": "mp-998890", "temperature": 1400, "hash": "2235029950651867963297679325649859467180845592703682588778830043857838720084731005530130774448941117984812105314377100338438254345966116730899395665552452", "id": "MD_2235029950651867963297679"}
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MD_2235029950651867963297679
| null |
[
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[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:03 |
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CO_7179193135890390350616031
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23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
"Wissam A. Saidi"
] |
Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
[
[
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/e0a72dd8
|
1904220718645702242283324793911551556742926495325235318604771290466389663335733339960140142842103190611994596330904809742090687846811396541628615785215145
|
DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
|
Cu16
|
Cu
|
A
|
[
29,
29,
29,
29,
29,
29,
29,
29,
29,
29,
29,
29,
29,
29,
29,
29
] |
[
"Cu"
] |
[
1
] | 1 | 16 |
[
[
5.346505165100098,
0,
0
],
[
0,
5.346505165100098,
0
],
[
0,
0,
5.504092216491699
]
] |
[
[
0.26078200340270996,
0.27612900733947754,
0.03499700129032135
],
[
0.2479580044746399,
0.24564500153064728,
2.8214080333709717
],
[
0.2171120047569275,
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0.05725700035691261
],
[
0.22169899940490723,
2.945542097091675,
2.800002098083496
],
[
2.928956985473633,
0.2501260042190552,
0.07092999666929245
],
[
2.932559013366699,
0.20801399648189545,
2.790954113006592
],
[
2.9196159839630127,
2.941222906112671,
0.0504239983856678
],
[
2.9357900619506836,
2.939462900161743,
2.8010919094085693
],
[
1.5742290019989014,
1.5989209413528442,
1.4404619932174683
],
[
1.5814800262451172,
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4.185470104217529
],
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1.533350944519043,
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1.5635080337524414,
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[
4.25756311416626,
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1.4315149784088135
],
[
4.2536702156066895,
1.5623290538787842,
4.1577229499816895
],
[
4.259586811065674,
4.253101825714111,
1.4043320417404175
],
[
4.251242160797119,
4.245326042175293,
4.196401119232178
]
] |
[
true,
true,
true
] |
[
1,
1,
1
] | 3 |
10485899437529245222856949313849083945519469398007589339640911251156652898011643999070786075779446918980150131576107035326388335972101846143877463503129537
| 1 |
VASP
|
DFT-PBE
| null |
[
[
-0.2812677025794983,
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0.09341985732316971
],
[
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],
[
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0.0842432975769043
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[
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0.07597663253545761,
-0.26190391182899475
],
[
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0.3038700520992279,
0.14505910873413086
],
[
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0.14962023496627808
],
[
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-0.07989399135112762,
-0.10509411245584488
],
[
0.16431552171707153,
0.028610190376639366,
0.06279151886701584
],
[
0.2698928117752075,
0.10941337794065475,
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],
[
0.4726302921772003,
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0.011308319866657257
],
[
0.27005648612976074,
-0.35525813698768616,
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],
[
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0.48836711049079895,
0.08381091058254242
],
[
-0.23560196161270142,
0.3025232255458832,
0.1935897171497345
],
[
0.10728730261325836,
0.2310551553964615,
0.14028961956501007
],
[
0.05945469066500664,
0.3295651972293854,
-0.023296870291233063
]
] | null |
[
[
43.916683197021484,
-0.07994300127029419,
-0.014465999789536
],
[
-0.07994300127029419,
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0.008542999625205994
],
[
-0.014465999789536,
0.008542999625205994,
45.89221954345703
]
] | true | null | null | -54.80109 | null | 0.386909 | 0.619551 |
{"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
| 2024-08-16T15:06:51 |
10804926451321809336967293430391096397635326401337409316273152947311277198010189398170772465595326180572196316309028802759181318806202246707815659522578692
|
PO_1080492645132180933696729
|
{"materials-project-id": "mp-1010136", "hash": "9023465436536084198450125260887540815347432608030255601143168665201972997876430378573817990288567142892702987114847153834845499711503008116889265087350967", "id": "MD_9023465436536084198450125"}
|
MD_9023465436536084198450125
| null |
[
"Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_3_0"
] |
[
"DS_dc3o40aou2le_0"
] | 2023-12-01T22:53:03 |
7905862986785370646519139913507667282464220713891865761746784638390951841587072836337415427732552582789981213671178551539729495844939522534910473425659158
|
CO_7905862986785370646519139
|
23-Single-Element-DNPs_RSCDD_2023-Cu
|
[
"Christopher M. Andolina",
"Wissam A. Saidi"
] |
Configurations of Cu 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.
|
[
"Cu"
] | 1 | 3,365 | 3,366 | 96,568 | 0 | 3,365 | 0 | 3,365 | 0 | 3,365 | -78.97025 | 1,825.396006 | 0 | 2023-12-01T17:53:36 |
[
[
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/e0a72dd8
|
1904220718645702242283324793911551556742926495325235318604771290466389663335733339960140142842103190611994596330904809742090687846811396541628615785215145
|
DS_dc3o40aou2le_0
|
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
|
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