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2023-12-01T22:53:03
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CO_7977259881433680910233236
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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2024-08-16T14:40:24
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CO_3164792535905641444298072
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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2024-08-16T15:07:21
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2023-12-01T22:53:03
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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.
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MD_1213582242930949697188121
2024-08-16T14:30:55
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2023-12-01T22:53:04
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CO_4417998126078009751487616
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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2024-08-16T15:19:09
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MD_2235029950651867963297679
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2023-12-01T22:53:03
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CO_1156666675907332130518232
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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MD_1213582242930949697188121
2024-08-16T14:51:46
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2023-12-01T22:53:03
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CO_1273579991810000420192362
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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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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:03:10
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PO_8853871859125456839452945
{"materials-project-id": "mp-1120774", "temperature": 1400, "hash": "4187850849357219619680007319790638112544181158251154606422114267951829843797785995974925221440397031764594530315848580188635845707901051979951294973736516", "id": "MD_4187850849357219619680007"}
MD_4187850849357219619680007
null
[ "Cu_iter2_6_mp-1120774_elastic_B222_dist03_7_1400_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9054797473653241335760059
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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1,825.396006
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2023-12-01T17:53:36
[ [ 1, 1, 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
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:09:17
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PO_8938474246708814103695051
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MD_7876487181888821678431936
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[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_2_11" ]
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2023-12-01T22:53:03
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CO_1480812903012077661613935
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:21:26
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2023-12-01T22:53:04
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CO_1289227276681155455286024
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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{'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
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:21:28
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PO_7540830083551456064094625
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MD_2235029950651867963297679
null
[ "Cu_iter1_4_mp-998890_elastic_B222_dist03_0_1400_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6237564128837166059506497
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:21:38
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_5_14" ]
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2023-12-01T22:53:03
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CO_2888750871638585708265181
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:18:05
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_7_5" ]
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2023-12-01T22:53:03
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CO_1735220601751725628984143
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
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MD_1213582242930949697188121
2024-08-16T14:18:38
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_11_14" ]
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2023-12-01T22:53:03
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CO_4185545229786162391919223
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
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2023-12-01T22:53:04
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CO_2356030580545525993297151
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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T14:49:48
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2023-12-01T22:53:03
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CO_1017662504569524985404918
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:37:40
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MD_7876487181888821678431936
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[ "Cu_iter0_T350_0_mp-30_vacancies_Vac_0_7" ]
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2023-12-01T22:53:03
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CO_4305638331005831599214933
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:01:33
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MD_7876487181888821678431936
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[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_4_12" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5923863329369441877014592
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:25:29
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2023-12-01T22:53:04
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CO_5402807714441375995831153
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:31:40
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PO_1330181104385353231223841
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_11_17" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1081483858627439238686546
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
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MD_1213582242930949697188121
2024-08-16T15:23:53
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PO_8520961337920643800929128
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_1_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1063359908743601719070089
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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2023-12-01T17:53:36
[ [ 1, 1, 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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:15:54
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PO_4339004957205140817684636
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_7_10" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2994690701143615697636274
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T15:28:46
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PO_1032643816782263403002075
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_5_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2503996243671944086299169
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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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
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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
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T15:37:33
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_7_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_4393531555845894778809645
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
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MD_1213582242930949697188121
2024-08-16T15:33:39
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_7_4" ]
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2023-12-01T22:53:04
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CO_4434370996138386674296426
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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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.
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:33:02
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MD_2235029950651867963297679
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2023-12-01T22:53:03
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CO_2936038316452583171679278
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T14:13:42
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_1_9" ]
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2023-12-01T22:53:03
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CO_1260375446849912370265974
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:42:22
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PO_5578918928961468404827183
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MD_4457740524666660225640342
null
[ "Cu_iter3_0_mp-30_elastic_B222_dist03_3_350_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7332561553087002605428031
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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2023-12-01T17:53:36
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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/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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:17:18
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PO_1417007142924577157452876
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_6_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6125707807872987337261721
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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2023-12-01T17:53:36
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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/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:45:40
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MD_8629312491804937715709257
null
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2023-12-01T22:53:03
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CO_6527447689001880014293880
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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MD_1213582242930949697188121
2024-08-16T14:20:18
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MD_9528764851809222476042710
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2023-12-01T22:53:03
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CO_5024590112572864530946973
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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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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:21:31
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PO_6743035086945400456102534
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_10_15" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1034016256686880167735014
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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MD_1213582242930949697188121
2024-08-16T14:31:18
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2023-12-01T22:53:03
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CO_1332717933216666405058067
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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T15:37:51
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MD_8629312491804937715709257
null
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2023-12-01T22:53:03
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CO_7640070872142385695275533
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:34:33
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MD_5096852878689515016501519
null
[ "Cu_iter1_4_mp-998890_elastic_B222_dist03_3_350_3" ]
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2023-12-01T22:53:03
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CO_5769905791647513683384777
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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MD_1213582242930949697188121
2024-08-16T14:39:26
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MD_7876487181888821678431936
null
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2023-12-01T22:53:04
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CO_4088881705276006182693902
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:01:40
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PO_5093355498267257620674853
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_3_5" ]
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2023-12-01T22:53:04
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CO_9741995412522491146449418
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:30:53
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_6_15" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1528485448589310367630965
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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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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:30:56
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PO_5725152859337209233495242
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_1_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7408980083522651608889481
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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3,365
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
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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
Cu
A
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CO_4131960629914008593197972
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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MD_1213582242930949697188121
2024-08-16T14:13:21
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MD_2235029950651867963297679
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2023-12-01T22:53:03
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CO_3933032111705269660205953
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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2024-08-16T14:59:32
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MD_7876487181888821678431936
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2023-12-01T22:53:04
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CO_6360016121117779943122589
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:58:13
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PO_6429037077990568645833083
{"materials-project-id": "mp-989695", "temperature": 1400, "hash": "10018648818121589343070919044496990525049041816415104835127439479605504624717308398728551986330500782301626532310527208491503520633227115180461767179631896", "id": "MD_1001864881812158934307091"}
MD_1001864881812158934307091
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_9_1400_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_6529672842775179882687184
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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2023-12-01T17:53:36
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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/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu31
Cu
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:47:48
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PO_3038453070096226169828132
{"materials-project-id": "mp-30", "hash": "7876487181888821678431936765949204563993039704321226337025456982326490818422212352824109087993382292227037837563119802330326948276325205728951171598043479", "id": "MD_7876487181888821678431936"}
MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_vacancies_Vac_0_18" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3462008081130977831379661
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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3,366
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3,365
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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
Cu16
Cu
A
[ 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29 ]
[ "Cu" ]
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1
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MD_1213582242930949697188121
2024-08-16T14:43:01
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PO_1238161885908174379005250
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_0_12" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7155039392685074843125715
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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2023-12-01T17:53:36
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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/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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:59:38
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PO_4621640014834648749565384
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_8_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9417813099607578708192892
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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2023-12-01T17:53:36
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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/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:23:17
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_6_16" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9550959893792517564840857
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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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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:57:30
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PO_1086729999164433750437278
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_2_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_8042025737431502200558057
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu33
Cu
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MD_1213582242930949697188121
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2023-12-01T22:53:03
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CO_4270803681776348756666778
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T14:38:25
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PO_1123716068584241955147829
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_4_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_4345818867268984517905896
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T15:31:23
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_4_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1191936098770094561358514
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:48:23
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PO_1032425916969991720757026
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_0_17" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2894507363919812888382911
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:24:35
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PO_1810622578987060278859745
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MD_2235029950651867963297679
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2023-12-01T22:53:04
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CO_8961613607493692148178377
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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MD_1213582242930949697188121
2024-08-16T15:25:18
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PO_6449747850569485929116289
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MD_9528764851809222476042710
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2023-12-01T22:53:03
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CO_1220161931552523371470943
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:58:08
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MD_8495636077538216939536635
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[ "Cu_iter1_0_mp-30_elastic_B222_dist03_7_1400_1" ]
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2023-12-01T22:53:03
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CO_8886100168909060276552025
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:24:21
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PO_8396962636303045975702435
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_11_19" ]
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2023-12-01T22:53:04
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CO_6775663941104724273552562
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:29:58
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PO_4655013181816782030370716
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MD_1001864881812158934307091
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[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_9_1400_6" ]
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2023-12-01T22:53:04
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CO_7449027464374319460673198
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T14:27:47
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PO_9695796046258809939235069
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_11_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_5229209130232216744627207
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:19:46
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PO_5993197475376484224889073
{"materials-project-id": "mp-30", "temperature": 1400, "hash": "8495636077538216939536635379244474204165680900104957820640201532737276966007943778867825222405747515176057416471562302984749176270215765774074301963875404", "id": "MD_8495636077538216939536635"}
MD_8495636077538216939536635
null
[ "Cu_iter3_0_mp-30_elastic_B222_dist03_5_1400_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3843906959600803103604470
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:33:44
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MD_9528764851809222476042710
null
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2023-12-01T22:53:03
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CO_1084139869957042322329611
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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{'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
Cu
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MD_1213582242930949697188121
2024-08-16T15:15:03
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PO_7346060862325573650819816
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_5_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6904218421183534335476322
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:39:15
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PO_1219450237520057646126240
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_5_1" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1301870895627280007880780
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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2023-12-01T17:53:36
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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/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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:11:50
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PO_2707652815704800283490238
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_2_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1020957626962490723748267
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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2023-12-01T17:53:36
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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/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:30:46
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2023-12-01T22:53:03
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CO_9566254907371891611831596
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:22:54
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MD_9023465436536084198450125
null
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2023-12-01T22:53:04
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CO_8383347498391918937851790
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T14:48:17
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PO_6183862636190857499350955
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_11_12" ]
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2023-12-01T22:53:03
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CO_1526250021751478719762140
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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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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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CO_7015539935134025596432452
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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MD_1213582242930949697188121
2024-08-16T14:57:01
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MD_9023465436536084198450125
null
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2023-12-01T22:53:03
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CO_6455046319488188739705554
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_4_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_5687636061765152733686878
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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MD_1213582242930949697188121
2024-08-16T14:41:14
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PO_1333268264119008563465555
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_5_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_2411767937869876797073766
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:35:18
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PO_3619619019099870987548229
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_10_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1046944188281311444697040
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu31
Cu
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2023-12-01T22:53:03
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CO_1182149773444733003658300
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:57:11
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MD_9023465436536084198450125
null
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2023-12-01T22:53:04
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CO_2052440980413779083882059
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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2024-08-16T15:23:38
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CO_1079086636050318621813164
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:40:57
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MD_9023465436536084198450125
null
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2023-12-01T22:53:03
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CO_8701558184828664640898114
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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2024-08-16T14:55:27
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_5_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1170224507802068529727072
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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T14:16:32
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MD_4187850849357219619680007
null
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[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_6130423455186202075649933
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:36:02
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PO_9140730697729070179258110
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MD_7387864771096776423525331
null
[ "Cu_iter1_3_mp-1010136_elastic_B222_dist03_3_1400_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_6941782181373987429239581
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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2023-12-01T17:53:36
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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}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:51:59
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[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_2_7" ]
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2023-12-01T22:53:03
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CO_7701947033340999024944320
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:13:00
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_9_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_6648398172658240622862486
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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2023-12-01T17:53:36
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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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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MD_1930707603777017050011070
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2023-12-01T22:53:04
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CO_5969783140156269849754303
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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MD_1213582242930949697188121
2024-08-16T14:49:27
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2023-12-01T22:53:03
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CO_1074885630376064648830787
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1001864881812158934307091
null
[ "Cu_iter1_1_mp-989695_elastic_B222_dist03_8_1400_2" ]
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2023-12-01T22:53:03
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CO_5724920693450736762695538
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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MD_1213582242930949697188121
2024-08-16T15:39:45
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MD_7876487181888821678431936
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[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_5_11" ]
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2023-12-01T22:53:04
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CO_2852215174207787432683485
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
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MD_1213582242930949697188121
2024-08-16T14:51:39
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_1_15" ]
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2023-12-01T22:53:03
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CO_2772064290590541600967877
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
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MD_1213582242930949697188121
2024-08-16T15:37:59
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PO_3003162648241312172658864
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_5_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7463257661130704232217396
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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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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:08:34
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PO_2916471652663998644403127
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_8_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1063156481707725934137614
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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3,365
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
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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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:27:32
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PO_2851052126594207567062257
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_6_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9419059102150499413681892
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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2023-12-01T17:53:36
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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/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:18:57
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PO_9428843745405828658114404
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_0_6" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5095609692324695298945888
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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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
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[ 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
Cu16
Cu
A
[ 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29 ]
[ "Cu" ]
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1
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MD_1213582242930949697188121
2024-08-16T14:36:30
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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.
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MD_1213582242930949697188121
2024-08-16T14:20:20
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MD_9023465436536084198450125
null
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2023-12-01T22:53:03
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CO_6180158565984424598110692
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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MD_1213582242930949697188121
2024-08-16T15:24:16
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MD_9303109585947760629038613
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2023-12-01T22:53:04
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CO_1006584491722233945937862
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu
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MD_1213582242930949697188121
2024-08-16T14:51:15
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2023-12-01T22:53:04
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CO_1016457103219971864977116
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:12:29
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MD_7876487181888821678431936
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[ "Cu_iter0_T1400_0_mp-30_interstitials_FCC2_14" ]
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2023-12-01T22:53:04
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CO_1271298952966701508823034
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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{'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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T14:42:18
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_1_15" ]
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2023-12-01T22:53:03
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CO_2791226111048591902196410
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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MD_1213582242930949697188121
2024-08-16T14:46:52
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MD_4457740524666660225640342
null
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2023-12-01T22:53:03
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CO_1123746864500018857320539
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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2023-12-01T17:53:36
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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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu15
Cu
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MD_1213582242930949697188121
2024-08-16T15:10:34
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MD_8629312491804937715709257
null
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2023-12-01T22:53:03
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CO_3423488888363665830667416
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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2023-12-01T17:53:36
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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
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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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.
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MD_1213582242930949697188121
2024-08-16T14:14:16
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_3_19" ]
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2023-12-01T22:53:03
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CO_3245318436022707757689852
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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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:10:19
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PO_6947043358516303539780672
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_4_17" ]
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2023-12-01T22:53:03
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CO_6297756972394862643711427
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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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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0