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Cu32
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:06:56
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PO_7440042461049174446955514
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_9_1400_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1226019380346875659110702
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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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
2024-08-16T14:35:30
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PO_2886565080895014916180041
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_10_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9638783610892411169326511
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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:38:44
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_10_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_8605425856538703123618693
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}
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
2024-08-16T14:55:50
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PO_3704625715173745768422935
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MD_2235029950651867963297679
null
[ "Cu_iter3_4_mp-998890_elastic_B222_dist03_3_1400_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1008190641255520490660169
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
2024-08-16T15:14:42
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PO_2007098833220582486825462
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_0_9" ]
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2023-12-01T22:53:03
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CO_5338226717434770400200275
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
2024-08-16T14:40:28
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PO_4022822315349342381352663
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_interstitials_FCC2_crowdion_1400_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1084320500655931372008935
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
Cu31
Cu
A
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MD_1213582242930949697188121
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CO_7087061742913914155029329
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
2024-08-16T15:15:44
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MD_5096852878689515016501519
null
[ "Cu_iter3_4_mp-998890_elastic_B222_dist03_3_350_7" ]
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2023-12-01T22:53:04
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CO_1327201854038317373806264
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
2024-08-16T15:23:29
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_4_18" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1162330051363922326781985
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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:52:49
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PO_5435758494118386465869056
{"materials-project-id": "mp-30", "temperature": 1400, "hash": "8495636077538216939536635379244474204165680900104957820640201532737276966007943778867825222405747515176057416471562302984749176270215765774074301963875404", "id": "MD_8495636077538216939536635"}
MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_4_1400_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1607580471858032841593234
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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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
2024-08-16T15:40:15
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PO_5816212206742122473662071
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_6_6" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3195725770333694704676236
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
1
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96,568
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3,365
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1,825.396006
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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
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:10:03
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PO_1440178808240095794779659
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_10_11" ]
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2023-12-01T22:53:04
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CO_9470761406360505196613074
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
2024-08-16T14:28:17
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PO_1136179035206890586774850
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MD_2235029950651867963297679
null
[ "Cu_iter1_4_mp-998890_elastic_B222_dist03_0_1400_6" ]
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2023-12-01T22:53:03
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CO_5237181889451596523652556
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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MD_1213582242930949697188121
2024-08-16T14:42:26
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PO_6998053230058498194685941
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_0_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6096199018193035893048603
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
2024-08-16T14:48:44
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_11_9" ]
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2023-12-01T22:53:03
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CO_8618697297565083315383277
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
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T15:22:15
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PO_2813613075487019617009785
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_2_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_8389150625184989744502809
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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A
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MD_1213582242930949697188121
2024-08-16T14:59:59
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PO_6016501514229787877822598
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_9_16" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_6087416732556256625330406
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
2024-08-16T14:41:41
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PO_1071089913014058265065330
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_6_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1121351316802579813841177
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
2024-08-16T14:34:07
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2023-12-01T22:53:03
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CO_2401558249165054082006735
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
2024-08-16T15:24:53
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_9_1" ]
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2023-12-01T22:53:03
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CO_1330215415804188324451621
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:13:55
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PO_9134665368511135507128374
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_1_15" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_5757234974399434317955533
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
2024-08-16T14:27:44
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PO_5192845767159637491466169
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_2_11" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_4731079313317715931111144
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
1
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3,366
96,568
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3,365
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3,365
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3,365
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1,825.396006
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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
Cu33
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:14:59
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PO_1821300390324814168283958
{"materials-project-id": "mp-30", "hash": "7876487181888821678431936765949204563993039704321226337025456982326490818422212352824109087993382292227037837563119802330326948276325205728951171598043479", "id": "MD_7876487181888821678431936"}
MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_interstitials_FCC2_db100_10" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1096673578611365960196524
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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2023
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:44:29
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PO_2215124804387765179384711
{"materials-project-id": "mp-30", "hash": "7876487181888821678431936765949204563993039704321226337025456982326490818422212352824109087993382292227037837563119802330326948276325205728951171598043479", "id": "MD_7876487181888821678431936"}
MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_4_6" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3710412181061368537613630
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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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
A
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[ "Cu" ]
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DFT-PBE
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MD_1213582242930949697188121
2024-08-16T14:30:28
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PO_7557928536413739508729572
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_vacancies_Vac_0_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6761314791832610917245194
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
1
3,365
3,366
96,568
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3,365
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3,365
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1,825.396006
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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
Cu32
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:35:50
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MD_9528764851809222476042710
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2023-12-01T22:53:04
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CO_2662002420582954954129842
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
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MD_1213582242930949697188121
2024-08-16T15:00:57
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_11_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_8479069102921274313981406
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
2024-08-16T14:48:54
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MD_1001864881812158934307091
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2023-12-01T22:53:04
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CO_1209809975313737425148043
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
2024-08-16T14:29:14
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MD_5096852878689515016501519
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2023-12-01T22:53:04
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CO_8139443478040700594919782
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
2024-08-16T15:33:28
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PO_1119575369400781666687808
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_7_16" ]
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2023-12-01T22:53:03
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CO_7723079716398614423492032
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
Cu48
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MD_1213582242930949697188121
2024-08-16T14:46:38
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PO_7887413987249299333898882
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_10_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1004265850256118170077204
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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:32:28
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PO_3503110979273376733519359
{"materials-project-id": "mp-989695", "hash": "9528764851809222476042710287434904452974295331793514297456432869021422888186293728034605436521615403216336741915436997874183701334979798960541055078530074", "id": "MD_9528764851809222476042710"}
MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_7_16" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7585766649059469275915918
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
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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
2024-08-16T14:25:49
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MD_9023465436536084198450125
null
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2023-12-01T22:53:03
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CO_5008903154806304079216666
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
2024-08-16T14:45:33
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_7_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_5311175349137608583274924
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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:34:10
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_interstitials_FCC2_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1144554667431462139685378
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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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
2024-08-16T15:29:25
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PO_1266516494581756026246387
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_5_1400_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_7490739986641313550757319
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
Cu48
Cu
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:06:21
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PO_8017508851599258771057801
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_1_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1081279964980397020294636
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
Cu32
Cu
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:21:44
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PO_1077300127275871119459011
{"materials-project-id": "mp-989695", "temperature": 350, "hash": "9303109585947760629038613527505414511528521115993097647212509458135776763896163175167973386081449718506314703600816088839828490974969951830609429564862999", "id": "MD_9303109585947760629038613"}
MD_9303109585947760629038613
null
[ "Cu_iter1_1_mp-989695_elastic_B222_dist03_4_350_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_4140081257110999295287764
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
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
2024-08-16T15:26:58
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PO_1157760583890368222168931
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_2_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_7610018932487899404054997
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
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MD_1213582242930949697188121
2024-08-16T15:13:25
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PO_4540063707185615836626161
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_9_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7848305867153555228376802
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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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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MD_1213582242930949697188121
2024-08-16T15:13:53
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MD_8629312491804937715709257
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2023-12-01T22:53:03
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CO_4749900981126061080564858
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
2024-08-16T14:28:55
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MD_8495636077538216939536635
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2023-12-01T22:53:04
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CO_4022499239191531247199177
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
2024-08-16T15:30:43
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PO_6914330798994377495696761
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_0_13" ]
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2023-12-01T22:53:03
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CO_6523473565282199372438721
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_7876487181888821678431936
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2023-12-01T22:53:03
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CO_5387896323498524669106204
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
2024-08-16T14:30:52
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_11_9" ]
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2023-12-01T22:53:03
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CO_7039876863271657392384417
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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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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2024-08-16T14:21:37
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2023-12-01T22:53:03
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CO_3689824666075099286504189
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
2024-08-16T15:09:59
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MD_8629312491804937715709257
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2023-12-01T22:53:04
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CO_4107955137727781575777246
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
2024-08-16T14:34:18
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MD_9023465436536084198450125
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2023-12-01T22:53:04
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CO_9405391716883073075541299
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
2024-08-16T15:18:27
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MD_7876487181888821678431936
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2023-12-01T22:53:03
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CO_8578495894911717725352394
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
A
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MD_1213582242930949697188121
2024-08-16T14:52:35
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PO_3994782655139227454817198
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_3_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3776354885104297923595976
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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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
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2023-12-01T22:53:04
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CO_1107954327670531087335148
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
2024-08-16T15:34:19
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_5_4" ]
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2023-12-01T22:53:03
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CO_4784290178148047212552563
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
2024-08-16T14:24:34
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2023-12-01T22:53:04
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CO_2999390223704570425980390
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
2024-08-16T15:09:09
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MD_2235029950651867963297679
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2023-12-01T22:53:04
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CO_1189608077516247664626435
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
Cu16
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MD_1213582242930949697188121
2024-08-16T14:46:04
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PO_8889769088278006823565583
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_6_13" ]
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2023-12-01T22:53:03
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CO_7573090503417217172126577
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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2023
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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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
2024-08-16T15:15:45
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PO_8983184699213674222556426
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MD_2235029950651867963297679
null
[ "Cu_iter1_4_mp-998890_elastic_B222_dist03_2_1400_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5732495953534666885290739
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
[ 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29 ]
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VASP
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MD_1213582242930949697188121
2024-08-16T15:26:39
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PO_5162706307013277680208531
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_9_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3513517040556079145417591
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "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
Cu48
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:36:16
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_11_18" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6415438464049392399780755
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
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:31:00
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PO_5722534633476232420591433
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_vacancies_Vac_0_1400_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_8014910613792848212034894
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
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:18:45
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PO_1066100765098735105979056
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MD_2235029950651867963297679
null
[ "Cu_iter1_4_mp-998890_elastic_B222_dist03_0_1400_1" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_7179193135890390350616031
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
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" ]
[ 1 ]
1
16
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1
VASP
DFT-PBE
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{"input": {"ALGO": "fast", "EDIFF": "1E-8", "ENCUT": "400", "IBRION": "0", "ICHARG": "0", "ISIF": "2", "ISMEAR": "1", "ISTART": "0", "ISYM": "0", "KBLOCK": "10", "KGAMMA": "F", "KPAR": "16", "KSPACING": "0.24", "LCHARG": "T", "LREAL": "F", "LWAVE": "F", "MAXMIX": "50", "NBLOCK": "1", "NELM": "200", "NELMIN": "4", "NPAR": "8", "POTIM": "2", "PREC": "A", "PSTRESS": "0", "SIGMA": "0.15", "SMASS": "-1", "file-type": "INCAR"}, "hash": "12135822429309496971881212100548754870478961791137930949625877367285087883313386750731592539663054471050405358418449513543996992751906584969573924479043013", "id": "MD_1213582242930949697188121"}
MD_1213582242930949697188121
2024-08-16T15:06:51
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PO_1080492645132180933696729
{"materials-project-id": "mp-1010136", "hash": "9023465436536084198450125260887540815347432608030255601143168665201972997876430378573817990288567142892702987114847153834845499711503008116889265087350967", "id": "MD_9023465436536084198450125"}
MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_3_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_7905862986785370646519139
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
1
3,365
3,366
96,568
0
3,365
0
3,365
0
3,365
-78.97025
1,825.396006
0
2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
1904220718645702242283324793911551556742926495325235318604771290466389663335733339960140142842103190611994596330904809742090687846811396541628615785215145
DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0