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Mo32
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[ "Mo" ]
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MD_1213582242930949697188121
2024-08-16T14:32:39
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MD_6966233607090115772682513
null
[ "Mo_iter_all_Mo_iter0_T750_3_mp-8637_elastic_B222_dist03_0_1" ]
[ "DS_bjkftn0ug9r3_0" ]
2023-12-01T22:55:06
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CO_1260684263818890814962785
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
Mo32
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MD_1213582242930949697188121
2024-08-16T14:16:05
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MD_6966233607090115772682513
null
[ "Mo_iter_all_Mo_iter0_T750_3_mp-8637_elastic_B222_dist03_10_19" ]
[ "DS_bjkftn0ug9r3_0" ]
2023-12-01T22:55:06
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CO_1357258917611737776326805
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:55:15
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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_bjkftn0ug9r3_0
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:40:46
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CO_3886930020026832290581450
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2024-08-16T14:44:59
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MD_3822345200460885929033732
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:23:57
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MD_1546279540348808226191426
null
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2023-12-01T22:55:06
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CO_2414513046061416837464995
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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2024-08-16T15:10:33
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MD_1546279540348808226191426
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2023-12-01T22:55:06
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:35:45
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MD_1218455025909496628366994
null
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2023-12-01T22:55:06
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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MD_1213582242930949697188121
2024-08-16T14:15:48
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MD_1086845055867124744315202
null
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2023-12-01T22:55:06
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:52:13
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MD_6966233607090115772682513
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2023-12-01T22:55:06
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CO_1175656134151919075398554
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2024-08-16T14:42:05
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_3822345200460885929033732
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CO_1006703069149455019868799
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2024-08-16T14:13:13
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MD_6966233607090115772682513
null
[ "Mo_iter_all_Mo_iter0_T3000_3_mp-8637_elastic_B222_dist03_6_2" ]
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T14:54:34
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MD_6966233607090115772682513
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CO_6767637063230638875760815
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
2024-08-16T15:41:12
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MD_1218455025909496628366994
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[ "Mo_iter_all_Mo_iter0_T3000_0_mp-129_interstitials_BCC2_interOh_0" ]
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2023-12-01T22:55:06
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CO_7788439993596592514534595
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
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[ "Mo_iter_all_Mo_iter0_T3000_4_mp-1066523_elastic_B222_dist03_4_14" ]
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CO_2917755931945695704856166
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
Mo16
Mo
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CO_8637828526973767997351539
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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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CO_5212895557562160015963214
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_bjkftn0ug9r3_0
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MD_1213582242930949697188121
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MD_1086845055867124744315202
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2023-12-01T22:55:06
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_bjkftn0ug9r3_0
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_1086845055867124744315202
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2023-12-01T22:55:06
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CO_7896850669672339718710514
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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[ [ 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_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
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2024-08-16T14:36:09
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MD_1546279540348808226191426
null
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2023-12-01T22:55:06
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CO_2600856150582782485101032
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
Mo16
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MD_1213582242930949697188121
2024-08-16T14:26:12
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MD_1218455025909496628366994
null
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23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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/b6ece7fd
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DS_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
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MD_1213582242930949697188121
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MD_1546279540348808226191426
null
[ "Mo_iter_all_Mo_iter0_T3000_6_mp-1056004_elastic_B222_dist03_8_1" ]
[ "DS_bjkftn0ug9r3_0" ]
2023-12-01T22:55:06
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CO_8282412397787791018493949
23-Single-Element-DNPs_RSCDD_2023-Mo
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:55:15
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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/b6ece7fd
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DS_bjkftn0ug9r3_0
23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0
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Dataset

23-Single-Element-DNPs RSCDD 2023-Mo

Description

Configurations of Mo from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.

Additional details stored in dataset columns prepended with "dataset_".

Dataset authors

Christopher M. Andolina, Wissam A. Saidi

Publication

https://doi.org/10.1039/D3DD00046J

Original data link

https://github.com/saidigroup/23-Single-Element-DNPs

License

gpl-3.0

Number of unique molecular configurations

3718

Number of atoms

66612

Elements included

Mo

Properties included

energy, atomic forces, cauchy stress

Cite this dataset

Andolina, C. M., and Saidi, W. A. 23-Single-Element-DNPs RSCDD 2023-Mo. ColabFit, 2023. https://doi.org/10.60732/b6ece7fd

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