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Cu48
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:12:28
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PO_7550502681665644940912277
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_2_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3617065578757391430705401
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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2023
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:00:50
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PO_2952125040784482542481754
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MD_9303109585947760629038613
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_5_350_4" ]
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CO_1019388541404535720073187
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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2024-08-16T14:55:40
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2023-12-01T22:53:04
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CO_7904699538641593819573168
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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2024-08-16T14:17:43
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2023-12-01T22:53:04
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CO_3573525435964434064067519
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:13:36
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MD_8629312491804937715709257
null
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2023-12-01T22:53:03
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CO_1110293426698551501622342
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:14:18
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PO_1107757924267723762305095
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_7_1" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1270864872637954814319388
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:23:12
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PO_1240168146630586059497074
{"materials-project-id": "mp-989695", "temperature": 350, "hash": "9303109585947760629038613527505414511528521115993097647212509458135776763896163175167973386081449718506314703600816088839828490974969951830609429564862999", "id": "MD_9303109585947760629038613"}
MD_9303109585947760629038613
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_3_350_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1192223045191087461419660
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
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2023
[ 1 ]
GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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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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1
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MD_1213582242930949697188121
2024-08-16T14:14:49
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PO_1241292186782315329824862
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MD_5096852878689515016501519
null
[ "Cu_iter3_4_mp-998890_elastic_B222_dist03_1_350_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_2843375257128372536818094
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T15:37:16
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_4_13" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9187437310528045751691232
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:16:09
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MD_9023465436536084198450125
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[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_6_18" ]
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2023-12-01T22:53:04
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CO_3905138599572619891029012
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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2024-08-16T15:02:52
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2023-12-01T22:53:04
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CO_1173544521887328751868605
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T15:08:37
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MD_8629312491804937715709257
null
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2023-12-01T22:53:04
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CO_1020656406197903909982116
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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MD_1213582242930949697188121
2024-08-16T14:57:18
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PO_6066513418297938584905137
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MD_7387864771096776423525331
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2023-12-01T22:53:04
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CO_1044567957748772054607071
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:55:10
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PO_2629979221602636943216877
{"materials-project-id": "mp-989695", "temperature": 1400, "hash": "10018648818121589343070919044496990525049041816415104835127439479605504624717308398728551986330500782301626532310527208491503520633227115180461767179631896", "id": "MD_1001864881812158934307091"}
MD_1001864881812158934307091
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_5_1400_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3723761964112252139372161
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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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DFT-PBE
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MD_1213582242930949697188121
2024-08-16T15:21:36
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PO_2421307634479717740840617
{"materials-project-id": "mp-989695", "hash": "9528764851809222476042710287434904452974295331793514297456432869021422888186293728034605436521615403216336741915436997874183701334979798960541055078530074", "id": "MD_9528764851809222476042710"}
MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_6_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_8383351552519681481825670
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
1
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1,825.396006
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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[ "Cu" ]
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1
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MD_1213582242930949697188121
2024-08-16T15:10:24
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2023-12-01T22:53:04
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CO_4364112333381537875244777
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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MD_1213582242930949697188121
2024-08-16T14:23:24
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MD_8629312491804937715709257
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[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_7_19" ]
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2023-12-01T22:53:04
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CO_6118871533693278223764069
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:17:42
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PO_4282934151222865782241172
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_0_4" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3581251192208527941984241
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
Cu
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:41:36
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PO_1068235645275134193783557
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_5_1400_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_3485207885711778967253438
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
Cu33
Cu
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MD_1213582242930949697188121
2024-08-16T14:38:48
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MD_8495636077538216939536635
null
[ "Cu_iter1_0_mp-30_interstitials_FCC2_db111_1400_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_6606676731701052707117167
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:31:01
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MD_5096852878689515016501519
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2023-12-01T22:53:03
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CO_1452128697963189074153643
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:25:47
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MD_2235029950651867963297679
null
[ "Cu_iter3_4_mp-998890_elastic_B222_dist03_3_1400_6" ]
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2023-12-01T22:53:04
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CO_7071008978499442624883939
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:24:35
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PO_8586969390098699174919801
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_7_4" ]
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2023-12-01T22:53:04
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CO_8265182627846324072280279
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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MD_1213582242930949697188121
2024-08-16T15:31:41
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PO_4592762433883454923379719
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_9_17" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1090345707359667931070409
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
2024-08-16T14:28:23
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PO_1009827448747244500587904
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_2_19" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9489782839119903190581052
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
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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
Cu48
Cu
A
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[ "Cu" ]
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1
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MD_1213582242930949697188121
2024-08-16T14:20:11
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PO_7932620286206559457641662
{"materials-project-id": "mp-1120774", "hash": "193070760377701705001107080539074290662545299088002219456431947370319665693130924919109129476617775689363633785947388360788009056473635193699639001226108", "id": "MD_1930707603777017050011070"}
MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_7_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_5742320230275692444044953
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:22:41
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PO_4102603516141591712803289
{"materials-project-id": "mp-30", "temperature": 350, "hash": "4457740524666660225640342330839629192576256311622761694375533923418256058007159657354259721073474972910168784282950146585048591205483032264513765211679111", "id": "MD_4457740524666660225640342"}
MD_4457740524666660225640342
null
[ "Cu_iter3_0_mp-30_elastic_B222_dist03_5_350_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7404121868553245956487726
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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 ]
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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
Cu32
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:50:26
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_1_19" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5614680970756206315056010
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
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MD_1213582242930949697188121
2024-08-16T15:22:53
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PO_4495081127530570209303514
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_8_17" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1037742521456993137816387
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:11:06
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PO_9615412593712547309950456
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MD_7876487181888821678431936
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2023-12-01T22:53:04
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CO_4261952899545637068492428
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:37:32
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_11_16" ]
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2023-12-01T22:53:03
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CO_6480956712678025424612917
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:15:58
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PO_5533008586268867390507723
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_2_19" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_4338426760556198944411181
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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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MD_1213582242930949697188121
2024-08-16T14:33:59
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MD_7876487181888821678431936
null
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2023-12-01T22:53:04
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CO_1122899102338377644530930
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:17:13
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MD_7876487181888821678431936
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[ "Cu_iter0_T350_0_mp-30_interstitials_FCC2_crowdion_6" ]
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2023-12-01T22:53:03
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CO_4706202570863772278429369
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:30:20
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PO_1094401842656404224575791
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MD_4457740524666660225640342
null
[ "Cu_iter3_0_mp-30_elastic_B222_dist03_1_350_1" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3442054378097999501709110
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:03:57
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PO_1960870142931482670441586
{"materials-project-id": "mp-30", "hash": "7876487181888821678431936765949204563993039704321226337025456982326490818422212352824109087993382292227037837563119802330326948276325205728951171598043479", "id": "MD_7876487181888821678431936"}
MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_5_15" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_6488824821397267730216594
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:35:37
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2023-12-01T22:53:03
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CO_4406661636449858401014008
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:17:48
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_4_0" ]
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2023-12-01T22:53:04
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CO_7969944773542861280508873
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:39:07
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MD_7876487181888821678431936
null
[ "Cu_iter0_T1400_0_mp-30_elastic_B222_dist03_4_18" ]
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2023-12-01T22:53:04
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CO_8107468353215046327723218
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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2023-12-01T17:53:36
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T14:52:37
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PO_1291107675673878053078166
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MD_4457740524666660225640342
null
[ "Cu_iter1_0_mp-30_elastic_B222_dist03_5_350_1" ]
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2023-12-01T22:53:03
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CO_6028319142703961976811423
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:27:01
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PO_8433296449620599735394255
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_10_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1044220137414454587528434
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:09:17
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PO_6202988950620757531423073
{"materials-project-id": "mp-989695", "temperature": 350, "hash": "9303109585947760629038613527505414511528521115993097647212509458135776763896163175167973386081449718506314703600816088839828490974969951830609429564862999", "id": "MD_9303109585947760629038613"}
MD_9303109585947760629038613
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_1_350_1" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_4499680104776596043268510
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:33:33
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PO_7176954580665124568912176
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_1_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1675292791430976259609539
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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VASP
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MD_1213582242930949697188121
2024-08-16T15:17:57
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MD_9528764851809222476042710
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2023-12-01T22:53:03
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CO_5818142843455622867643583
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:41:46
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MD_7876487181888821678431936
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2023-12-01T22:53:04
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CO_2976798903625622097911237
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:55:14
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PO_9759753059475047992325283
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_5_15" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1114100338470252195075954
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:26:19
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_5_13" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9317916409379209156141371
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:12:51
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PO_1098832838264482086743769
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MD_5096852878689515016501519
null
[ "Cu_iter3_4_mp-998890_elastic_B222_dist03_1_350_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_5324654637167400987481865
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:37:25
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MD_9528764851809222476042710
null
[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_6_13" ]
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2023-12-01T22:53:03
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CO_9863973762493811721626158
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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MD_1213582242930949697188121
2024-08-16T15:39:23
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MD_9303109585947760629038613
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2023-12-01T22:53:04
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CO_4511630430857739777710260
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:27:30
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MD_9528764851809222476042710
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[ "Cu_iter0_T350_1_mp-989695_elastic_B222_dist03_7_18" ]
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2023-12-01T22:53:04
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CO_1096576430515635607220654
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:23:55
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PO_2740379570685700724587396
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_vacancies_Vac_0_5" ]
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2023-12-01T22:53:03
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CO_8784773458858589080741403
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:52:07
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_6_15" ]
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2023-12-01T22:53:04
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CO_1027752426710566271249923
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:38:07
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PO_3677940213033616552224757
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MD_1001864881812158934307091
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_1_1400_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1316438447003142717928191
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:26:37
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PO_3638341091957082850735041
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_6_14" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7305292234368285980888901
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu33
Cu
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MD_1213582242930949697188121
2024-08-16T14:51:23
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2023-12-01T22:53:03
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CO_3509370249354365222683215
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:46:05
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MD_9528764851809222476042710
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2023-12-01T22:53:03
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CO_3775422064891039513813073
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:21:52
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PO_1582373635067168742374482
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_9_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_4355329833406675567034270
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
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23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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MD_1213582242930949697188121
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2023-12-01T22:53:03
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CO_1135938482002100086237466
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_1_14" ]
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2023-12-01T22:53:04
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CO_6126317938553278862976031
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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Cu15
Cu
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MD_1213582242930949697188121
2024-08-16T14:18:56
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2023-12-01T22:53:03
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CO_4110412426164945765218605
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:05:40
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PO_9228496860070129578962094
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MD_8629312491804937715709257
null
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2023-12-01T22:53:03
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CO_1021590151445406560229343
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:18:23
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MD_7876487181888821678431936
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2023-12-01T22:53:04
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CO_1078038380046895861696648
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:35:48
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MD_9528764851809222476042710
null
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2023-12-01T22:53:03
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CO_1392333068744313217721367
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:54:58
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_7_19" ]
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2023-12-01T22:53:04
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CO_1005459586543259143337112
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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}
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
2024-08-16T14:57:09
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MD_9528764851809222476042710
null
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2023-12-01T22:53:04
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CO_8937566957802977309548690
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T14:13:44
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PO_1285559521825449152188512
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MD_7387864771096776423525331
null
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2023-12-01T22:53:04
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CO_1118023024143404782662618
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:45:19
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2023-12-01T22:53:03
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CO_1060191900517448388963110
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:50:20
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PO_8553033443809375779648693
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MD_9023465436536084198450125
null
[ "Cu_iter0_T1400_3_mp-1010136_elastic_B222_dist03_0_16" ]
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2023-12-01T22:53:03
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CO_8757004471350179189653629
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:51:29
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_3_5" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1288585855129866354690547
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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2023-12-01T17:53:36
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DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:12:32
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null
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2023-12-01T22:53:04
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CO_1482544035428552045102423
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
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MD_1213582242930949697188121
2024-08-16T14:35:36
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MD_9023465436536084198450125
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2023-12-01T22:53:04
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CO_4599039211086250654141392
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:40:14
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_5_19" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_5650497170702385399406058
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:44:22
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MD_8495636077538216939536635
null
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2023-12-01T22:53:04
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CO_5384827534700761388961548
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:54:30
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PO_9304033137649097164521451
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MD_7387864771096776423525331
null
[ "Cu_iter3_3_mp-1010136_elastic_B222_dist03_3_1400_10" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_7719162010993943820899620
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:46:42
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MD_9023465436536084198450125
null
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2023-12-01T22:53:03
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CO_1106608714499250652201222
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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MD_1213582242930949697188121
2024-08-16T14:58:51
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MD_7876487181888821678431936
null
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2023-12-01T22:53:04
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CO_5498127625867824416561015
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:47:24
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PO_7266300643817207603491111
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MD_2235029950651867963297679
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[ "Cu_iter1_4_mp-998890_elastic_B222_dist03_5_1400_2" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1293662071614434773447156
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T15:32:35
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PO_1291933665839330598270524
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_1_17" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1181384984439177592824836
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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[ "Cu" ]
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DFT-PBE
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MD_1213582242930949697188121
2024-08-16T14:58:23
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PO_5567231060867075355064013
{"materials-project-id": "mp-30", "hash": "7876487181888821678431936765949204563993039704321226337025456982326490818422212352824109087993382292227037837563119802330326948276325205728951171598043479", "id": "MD_7876487181888821678431936"}
MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_11_16" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_7296071867135990308053835
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
0
3,365
0
3,365
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1,825.396006
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2023-12-01T17:53:36
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
A
[ 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29 ]
[ "Cu" ]
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1
16
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MD_1213582242930949697188121
2024-08-16T14:17:44
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MD_8629312491804937715709257
null
[ "Cu_iter0_T350_4_mp-998890_elastic_B222_dist03_11_1" ]
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2023-12-01T22:53:04
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CO_3653366332649503824698003
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:17:14
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_2_19" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_2831814242592256222809413
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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MD_1213582242930949697188121
2024-08-16T14:23:01
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MD_1930707603777017050011070
null
[ "Cu_iter0_T1400_6_mp-1120774_elastic_B222_dist03_1_11" ]
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2023-12-01T22:53:04
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CO_6438583163369147927148485
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
Cu15
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MD_1213582242930949697188121
2024-08-16T15:23:08
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PO_7018849930534290415194044
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MD_8629312491804937715709257
null
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2023-12-01T22:53:03
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CO_2689270969691215149238176
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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GPL-3.0-only
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23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
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MD_1213582242930949697188121
2024-08-16T15:09:30
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PO_3148251818010438966512399
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MD_9528764851809222476042710
null
[ "Cu_iter0_T1400_1_mp-989695_elastic_B222_dist03_6_12" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3768347626922320440780104
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:27:56
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PO_1319382353492800889234346
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MD_9303109585947760629038613
null
[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_9_350_0" ]
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2023-12-01T22:53:04
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CO_1209469647725054453671972
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:39:53
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_11_0" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_4356685233829683110082899
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
Cu48
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MD_1213582242930949697188121
2024-08-16T14:48:55
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_3_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_1725723661545381299941617
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:19:43
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PO_1021084576544265156203049
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_9_7" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_1097710273720536593437139
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:15:41
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PO_6379010565345695359421538
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MD_9023465436536084198450125
null
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2023-12-01T22:53:03
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CO_4156877452903078141946619
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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MD_1213582242930949697188121
2024-08-16T14:31:48
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MD_8495636077538216939536635
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2023-12-01T22:53:03
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CO_9475215951268202714717488
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:22:04
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PO_6929996527632876536229416
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MD_9023465436536084198450125
null
[ "Cu_iter0_T350_3_mp-1010136_elastic_B222_dist03_2_3" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_8532111697346037506541370
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:54:34
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PO_2910753524556900892939035
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MD_7387864771096776423525331
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[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3644703737565330398744590
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
A
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MD_1213582242930949697188121
2024-08-16T14:12:20
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MD_9303109585947760629038613
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[ "Cu_iter3_1_mp-989695_elastic_B222_dist03_5_350_9" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_3375217769502176632235022
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Cu" ]
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2023-12-01T17:53:36
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu32
Cu
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MD_1213582242930949697188121
2024-08-16T14:26:46
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PO_1653747384622971540806842
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MD_7876487181888821678431936
null
[ "Cu_iter0_T350_0_mp-30_elastic_B222_dist03_3_8" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:04
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CO_9135920510327153851445245
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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GPL-3.0-only
{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/e0a72dd8
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu48
Cu
A
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[ "Cu" ]
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MD_1213582242930949697188121
2024-08-16T14:21:24
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PO_7020085071601318395463102
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MD_1930707603777017050011070
null
[ "Cu_iter0_T350_6_mp-1120774_elastic_B222_dist03_1_17" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_4649083409289277695795703
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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
Cu32
Cu
A
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MD_1213582242930949697188121
2024-08-16T15:03:36
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MD_1001864881812158934307091
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2023-12-01T22:53:04
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CO_4392393510520035782258238
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:52:53
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MD_9528764851809222476042710
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2023-12-01T22:53:03
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CO_4978513858794409577757365
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
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DS_dc3o40aou2le_0
23-Single-Element-DNPs_RSCDD_2023-Cu__Andolina-Saidi__DS_dc3o40aou2le_0
Cu16
Cu
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MD_1213582242930949697188121
2024-08-16T15:40:40
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PO_6671229221010710670969671
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MD_8629312491804937715709257
null
[ "Cu_iter0_T1400_4_mp-998890_elastic_B222_dist03_5_18" ]
[ "DS_dc3o40aou2le_0" ]
2023-12-01T22:53:03
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CO_9293015077209171698319560
23-Single-Element-DNPs_RSCDD_2023-Cu
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Cu from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (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