Datasets:
colabfit
/
23-Single-Element-DNPs_RSCDD_2023-Cu

Dataset card Data Studio Files Community
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Cu32
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2024-08-16T15:12:51
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2023-12-01T22:53:03
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CO_7139950830102132262423242
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
Cu16
Cu
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2023-12-01T22:53:03
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CO_1336939253376728286645958
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
Cu48
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MD_1213582242930949697188121
2024-08-16T14:52:42
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MD_4187850849357219619680007
null
[ "Cu_iter2_6_mp-1120774_elastic_B222_dist03_11_1400_0" ]
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2023-12-01T22:53:04
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CO_1246258292339937102570195
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
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MD_1213582242930949697188121
2024-08-16T14:19:08
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PO_1755035362804265298696918
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_4_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_6441373762821235820900270
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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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}
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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
2024-08-16T15:38:22
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_10_13" ]
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2023-12-01T22:53:03
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CO_4830826059150690647041555
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
2024-08-16T14:16:47
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MD_1930707603777017050011070
null
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2023-12-01T22:53:03
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CO_1192453828688932034772080
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
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MD_1213582242930949697188121
2024-08-16T15:27:38
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[ "Cu_iter1_3_mp-1010136_elastic_B222_dist03_5_1400_8" ]
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2023-12-01T22:53:04
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CO_7413808459768416174513462
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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Cu16
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MD_1213582242930949697188121
2024-08-16T14:22:02
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_0_2" ]
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2023-12-01T22:53:03
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CO_5093461323492879148664282
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
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:55:44
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_7_12" ]
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2023-12-01T22:53:04
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CO_2310450849512692910395051
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T15:09:46
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PO_9055750788648637949590293
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_6_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2091906457561438983031661
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
2024-08-16T14:33:59
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PO_9351891632462639994170790
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_0_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6216120160795899936996335
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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[ 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
2024-08-16T15:25:33
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PO_1081430831673456909353853
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_0_13" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1117593842874372617702297
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
2024-08-16T15:34:11
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PO_1815787130792800715893490
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_11_11" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6561192392415156470759205
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:27:33
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MD_8495636077538216939536635
null
[ "Cu_iter3_0_mp-30_elastic_B222_dist03_1_1400_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1101798866243624643955048
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:19:07
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PO_1102869646157321404769955
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_10_11" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2343265716765329506099888
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
A
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MD_1213582242930949697188121
2024-08-16T15:27:46
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_7_19" ]
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2023-12-01T22:53:04
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CO_8793769790723227005206462
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
2024-08-16T14:53:26
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_11_15" ]
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2023-12-01T22:53:03
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CO_1080629870196823956187409
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:48:06
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_9_6" ]
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2023-12-01T22:53:04
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CO_4962560734914291043917469
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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Cu
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MD_1213582242930949697188121
2024-08-16T15:41:46
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PO_2689428243503542700749369
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MD_7387864771096776423525331
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[ "Cu_iter1_3_mp-1010136_elastic_B222_dist03_2_1400_2" ]
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2023-12-01T22:53:03
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CO_7326417701981244667043988
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
2024-08-16T15:38:52
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PO_7625105927159869765647620
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MD_7900197416090488123862896
null
[ "Cu_iter3_3_mp-1010136_elastic_B222_dist03_1_350_3" ]
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2023-12-01T22:53:04
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CO_6126206757698072868295628
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
2024-08-16T15:29:31
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MD_1930707603777017050011070
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2023-12-01T22:53:03
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CO_3099026604620693571921046
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
2024-08-16T15:39:53
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MD_2235029950651867963297679
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2023-12-01T22:53:03
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CO_7931659387776098973675533
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:29:19
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PO_8407794502228936197272813
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MD_5096852878689515016501519
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[ "Cu_iter1_4_mp-998890_elastic_B222_dist03_3_350_0" ]
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2023-12-01T22:53:03
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CO_1327593184331624488826941
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:10:14
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PO_9192858020579310821553382
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MD_4457740524666660225640342
null
[ "Cu_iter3_0_mp-30_elastic_B222_dist03_5_350_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9458003387282796286842950
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
2024-08-16T14:24:01
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PO_4665016150374183755873805
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_5_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9028563952678273928525090
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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MD_1213582242930949697188121
2024-08-16T15:36:13
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_9_5" ]
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2023-12-01T22:53:04
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CO_3470351018513654115217975
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
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:13:06
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_4_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1277280966082563096390022
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
Cu48
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:54:00
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MD_1930707603777017050011070
null
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2023-12-01T22:53:04
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CO_1921590639432044469253116
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T14:14:12
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_4_2" ]
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2023-12-01T22:53:04
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CO_1130510795438284381898952
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
2024-08-16T15:38:52
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PO_2529817916605595218227116
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MD_8629312491804937715709257
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2023-12-01T22:53:03
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CO_6319008496789653367786495
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
2024-08-16T14:28:16
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MD_7387864771096776423525331
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2023-12-01T22:53:04
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CO_9344594208296404957854238
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:13:38
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PO_3526242305429526779194813
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_6_5" ]
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2023-12-01T22:53:04
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CO_1340067597537416072122088
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
Cu33
Cu
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MD_1213582242930949697188121
2024-08-16T14:11:59
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PO_1106928620348235568102879
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MD_4457740524666660225640342
null
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2023-12-01T22:53:04
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CO_1199626634121736878972562
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:17:42
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PO_2816523312195939062983727
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_7_14" ]
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2023-12-01T22:53:03
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CO_1296311288457822357228700
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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:15:39
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PO_1272268007252988931544451
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_10_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
1597098538727669782537430877742198779034985870402068306007905363254655249921502933638447183367486826140362657888970353019609806357649831329192165651841912
CO_1597098538727669782537430
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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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T14:46:50
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_9_17" ]
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2023-12-01T22:53:04
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CO_9498408765707091772951838
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
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MD_1213582242930949697188121
2024-08-16T15:16:42
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PO_8601789931544784079710538
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MD_9303109585947760629038613
null
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2023-12-01T22:53:04
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CO_2314447292706158885991442
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
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MD_1213582242930949697188121
2024-08-16T14:29:48
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PO_4289801919601579913679373
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_2_6" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3645374516730741820530887
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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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:25:24
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PO_9013084940983358165471345
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_3_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1212791864217234212017674
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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1,825.396006
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
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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
2024-08-16T14:55:36
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_7_13" ]
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2023-12-01T22:53:04
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CO_1860606731573015321458465
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
2024-08-16T15:03:42
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PO_1293968532473604708608835
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_4_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1299089563798965455743434
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
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MD_1213582242930949697188121
2024-08-16T15:18:31
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MD_1930707603777017050011070
null
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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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{'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
2024-08-16T14:38:27
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_5_0" ]
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2023-12-01T22:53:04
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CO_7500616420234269503919647
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:05:32
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PO_4519186400178894737972320
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_8_0" ]
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2023-12-01T22:53:03
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CO_1311888629359230213489265
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:44:20
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2023-12-01T22:53:04
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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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{'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
2024-08-16T14:56:53
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_5_17" ]
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2023-12-01T22:53:03
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CO_6030548822994762108771108
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
2024-08-16T14:34:02
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PO_6659253679140477291480045
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MD_7387864771096776423525331
null
[ "Cu_iter1_3_mp-1010136_elastic_B222_dist03_3_1400_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3800761444602673504786632
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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MD_1213582242930949697188121
2024-08-16T14:51:41
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PO_1275443583935480439628589
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_10_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1000743062810670923091534
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
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MD_1213582242930949697188121
2024-08-16T14:27:54
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PO_8382102849887570900183639
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MD_1001864881812158934307091
null
[ "Cu_iter1_1_mp-989695_elastic_B222_dist03_5_1400_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1020496350894624368246927
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T14:12:01
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_9_15" ]
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2023-12-01T22:53:04
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CO_1129219410341199741187659
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
2024-08-16T15:00:55
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MD_9303109585947760629038613
null
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2023-12-01T22:53:04
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CO_4201043605541230309250406
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
2024-08-16T15:01:01
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MD_8629312491804937715709257
null
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2023-12-01T22:53:03
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CO_4884874211103481306767320
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:07:52
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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
2024-08-16T15:38:38
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_8_6" ]
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2023-12-01T22:53:04
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CO_9243988661313612760049905
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
Cu
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MD_1213582242930949697188121
2024-08-16T15:31:04
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PO_1579249998882400733593557
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_1_0" ]
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2023-12-01T22:53:04
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CO_9000813513494249943704515
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
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MD_1213582242930949697188121
2024-08-16T14:58:46
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MD_9528764851809222476042710
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[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9422211984090300769612469
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
2024-08-16T15:30:32
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PO_1232336605083871748393169
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MD_2235029950651867963297679
null
[ "Cu_iter3_4_mp-998890_elastic_B222_dist03_5_1400_0" ]
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2023-12-01T22:53:04
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CO_2560174822747882784529003
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
Cu
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MD_1213582242930949697188121
2024-08-16T14:55:31
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PO_7480071055315751336738475
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_10_9" ]
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2023-12-01T22:53:04
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CO_1318976753701672967114572
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
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MD_1213582242930949697188121
2024-08-16T15:24:55
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MD_1001864881812158934307091
null
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2023-12-01T22:53:04
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CO_1197634805401847692937182
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
Cu15
Cu
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MD_1213582242930949697188121
2024-08-16T15:18:00
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PO_2904706162377042709290951
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MD_7387864771096776423525331
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[ "Cu_iter1_3_mp-1010136_vacancies_Vac_0_1400_1" ]
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2023-12-01T22:53:04
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CO_7960094046698362730881830
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
A
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MD_1213582242930949697188121
2024-08-16T14:55:00
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PO_8107573451816683987072516
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MD_5096852878689515016501519
null
[ "Cu_iter3_4_mp-998890_elastic_B222_dist03_3_350_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_5982143666315748940333071
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
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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
2024-08-16T15:16:25
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PO_3973441466909586615933007
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_8_11" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1282999603663519246367842
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:39:04
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MD_7876487181888821678431936
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2023-12-01T22:53:04
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CO_1307271967116438390156791
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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MD_1213582242930949697188121
2024-08-16T14:15:39
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MD_1001864881812158934307091
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2023-12-01T22:53:04
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CO_2716105220512827424938402
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
2024-08-16T14:39:39
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_8_1" ]
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2023-12-01T22:53:03
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CO_1280929154353755141413157
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:15:29
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PO_1884528861602183858966283
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_5_18" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3607507241190724156681093
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
Cu33
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MD_1213582242930949697188121
2024-08-16T15:01:38
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PO_1424075726564785394292606
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_interstitials_FCC2_interOh_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7215398636959350633907408
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
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:38:00
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PO_7916072062784243045639316
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_8_1" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
522809089168531518948090318197205207764839193663877167408792852858878571569572846303709370119843884492907714978256712407482261774930666536137073778048095
CO_5228090891685315189480903
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
0
3,365
0
3,365
-78.97025
1,825.396006
0
2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
[ 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29 ]
[ "Cu" ]
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DFT-PBE
null
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MD_1213582242930949697188121
2024-08-16T14:27:42
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PO_8490665002287502928593043
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MD_4187850849357219619680007
null
[ "Cu_iter2_6_mp-1120774_elastic_B222_dist03_2_1400_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9819105927576138959844888
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:08:28
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PO_3920838606284705311911904
{"materials-project-id": "mp-30", "hash": "7876487181888821678431936765949204563993039704321226337025456982326490818422212352824109087993382292227037837563119802330326948276325205728951171598043479", "id": "MD_7876487181888821678431936"}
MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_0_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9058491528381607101353187
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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MD_1213582242930949697188121
2024-08-16T14:51:03
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PO_8994683073716444684629481
{"materials-project-id": "mp-30", "temperature": 1400, "hash": "8495636077538216939536635379244474204165680900104957820640201532737276966007943778867825222405747515176057416471562302984749176270215765774074301963875404", "id": "MD_8495636077538216939536635"}
MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_11_1400_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_4267576267822897904539195
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
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MD_1213582242930949697188121
2024-08-16T14:42:29
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PO_4090921085110256465570820
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_9_14" ]
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2023-12-01T22:53:03
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CO_8726357267354615971793547
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
2024-08-16T15:33:41
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PO_1085574550837985248198473
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_4_17" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7222467759274190448766031
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
Cu33
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MD_1213582242930949697188121
2024-08-16T14:21:57
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2023-12-01T22:53:03
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CO_4362094944519892161690530
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
2024-08-16T15:11:17
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2023-12-01T22:53:03
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CO_2011225009508349923250168
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
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:46:15
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PO_1174347866047708052259836
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_8_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9384509635586436367860442
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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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
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:19:38
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_9_1400_1" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2387361649565417638967547
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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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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MD_1213582242930949697188121
2024-08-16T15:40:52
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_interstitials_FCC2_db110_1400_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_8974114676462984589853994
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
2024-08-16T14:16:31
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PO_4758298725145102138130714
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_11_12" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5109187899943264364634980
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
2024-08-16T14:38:36
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PO_1128105800446754877913896
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MD_7876487181888821678431936
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[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_10_10" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5508683170052189851296000
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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CO_6980569152292502152129928
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T14:16:51
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PO_6581875396027056960982589
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_4_11" ]
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2023-12-01T22:53:03
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CO_2591759308167909549281013
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
2024-08-16T14:14:54
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_2_1400_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1090945909685865356808145
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
2024-08-16T15:23:31
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PO_4013052204804255712043676
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_8_6" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1087154678993393605842903
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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10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:04:08
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_5_5" ]
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2023-12-01T22:53:03
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CO_1222103597441399558464356
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:36:29
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PO_9249070486553959552480743
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MD_1001864881812158934307091
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_3_1400_6" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6822992125026228378202823
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:10:52
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PO_5189452031212758748544298
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_4_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5836434089108340801568128
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:38:28
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2023-12-01T22:53:03
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CO_8536014590768441379723859
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:23:46
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PO_1052980250431417670470447
{"materials-project-id": "mp-1010136", "temperature": 350, "hash": "7900197416090488123862896789117493423581871777419387864084423922449774980290747890188047965330050746826773533599991796916336997251872827143098361014527782", "id": "MD_7900197416090488123862896"}
MD_7900197416090488123862896
null
[ "Cu_iter3_3_mp-1010136_elastic_B222_dist03_3_350_4" ]
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2023-12-01T22:53:04
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CO_6345586027591298338295176
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
2024-08-16T14:37:41
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_9_13" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_4114435480135147772017768
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
Cu48
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:46:02
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PO_1139220211121279155688399
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_11_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1060995839555530120365309
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
Cu32
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:37:01
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MD_9528764851809222476042710
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2023-12-01T22:53:03
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CO_4273099399145481995411512
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
2024-08-16T15:34:49
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_2_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2103072861765668473771955
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
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MD_1213582242930949697188121
2024-08-16T14:34:19
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2023-12-01T22:53:04
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CO_1008072221072908712930491
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T14:49:53
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PO_1311007306729889211668020
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_4_8" ]
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2023-12-01T22:53:04
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CO_1310454874386944848842968
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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MD_1213582242930949697188121
2024-08-16T14:17:10
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PO_4055126716671119072513761
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_2_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3748023279414804435644244
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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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
2024-08-16T15:01:05
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PO_4456722636837046707899287
{"materials-project-id": "mp-30", "hash": "7876487181888821678431936765949204563993039704321226337025456982326490818422212352824109087993382292227037837563119802330326948276325205728951171598043479", "id": "MD_7876487181888821678431936"}
MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_11_12" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3478749817593656250051373
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
2024-08-16T15:30:29
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PO_6521827061282962145962339
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_4_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
4163301866319028971387582880049996740329404042951818084276913626688939661025084697500404416545636114438466097792173945633109178674182182107946016908230243
CO_4163301866319028971387582
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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1,825.396006
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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[ "Cu" ]
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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
2024-08-16T14:40:22
11388119110504042519343051673570040949477152866010983948702112821138172157926738922894602284458381324728083685681320455775936304660748844867012208957609554
PO_1138811911050404251934305
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_10_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
5363119188402812493997453354733008991265556690972649011203907237119225407403121600174819823742306029916706193389382679585435632802800267155273714441800635
CO_5363119188402812493997453
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
3,366
96,568
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3,365
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3,365
0
3,365
-78.97025
1,825.396006
0
2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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[ "Cu" ]
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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
2024-08-16T14:42:43
11409018451504676590206199992607265610087500251233019816471429426973866245828239892690184939222426316063729484815587279419889188536070556250741956627348922
PO_1140901845150467659020619
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_1_6" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
5338741545859487192820350936193849014482967186689478594342655974010177628940544221338713570611200375472343534908742013696521189457111366545124853510435546
CO_5338741545859487192820350
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