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

Dataset card Data Studio Files Community
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23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T22:58:16
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CO_9676536588483769431425870
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
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MD_1213582242930949697188121
2024-08-16T15:42:05
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MD_8097482567437995173613322
null
[ "Re_iter1_sc_elastic_B222_dist03_5_875_17" ]
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2023-12-01T22:58:16
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CO_9691688818526839766959177
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
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MD_1213582242930949697188121
2024-08-16T15:41:20
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PO_1974810446804160412725965
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[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9707805414828089854846227
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re17
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MD_1213582242930949697188121
2024-08-16T14:50:25
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MD_3140906615584827818889926
null
[ "Re_iter0_T875_0_mp-8_interstitials_HCP2_crowdion_18" ]
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2023-12-01T22:58:16
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CO_9720021305697110782833910
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
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MD_1213582242930949697188121
2024-08-16T15:11:44
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PO_9415587445055500655318659
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MD_2333412701806194962250672
null
[ "Re_iter2_3_mp-8642_elastic_B222_dist03_2_3500_0" ]
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2023-12-01T22:58:16
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CO_9722614627979452360990046
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re17
Re
A
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MD_1213582242930949697188121
2024-08-16T15:38:09
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MD_1951564499902567866502008
null
[ "Re_iter1_0_mp-8_interstitials_HCP2_crowdionB_875_14" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9726592146768095272813349
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re32
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MD_1213582242930949697188121
2024-08-16T14:48:25
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2023-12-01T22:58:16
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23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
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MD_1213582242930949697188121
2024-08-16T15:41:44
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MD_3140906615584827818889926
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2023-12-01T22:58:16
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CO_9727643581996357371580279
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re17
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MD_1213582242930949697188121
2024-08-16T15:33:45
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PO_5927036219995090541112403
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MD_3894327761601644436733559
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[ "Re_iter2_0_mp-8_interstitials_HCP2_crowdionB2_3500_0" ]
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2023-12-01T22:58:16
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CO_9732050298297601369703175
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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
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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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re32
Re
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MD_1213582242930949697188121
2024-08-16T15:15:12
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MD_5276451162749980934843793
null
[ "Re_iter0_T875_3_mp-8642_elastic_B222_dist03_8_3" ]
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2023-12-01T22:58:16
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CO_9753361575799500245092389
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re17
Re
A
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MD_1213582242930949697188121
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MD_3894327761601644436733559
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[ "Re_iter1_0_mp-8_interstitials_HCP2_crowdionB_3500_9" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9762287103044521294725503
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
[ "Re" ]
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re8
Re
A
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MD_1213582242930949697188121
2024-08-16T15:00:06
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MD_3580205984756795750502668
null
[ "Re_iter2_sc_elastic_B222_dist03_6_3500_4" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9829473005838317002816505
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re32
Re
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MD_1213582242930949697188121
2024-08-16T15:39:42
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MD_4820518588507787498309475
null
[ "Re_iter2_3_mp-8642_elastic_B222_dist03_5_875_2" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9830491610774508094125001
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re16
Re
A
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MD_1213582242930949697188121
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MD_3140906615584827818889926
null
[ "Re_iter0_T3500_0_mp-8_elastic_B222_dist03_5_16" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9880242427964580117343277
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re8
Re
A
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MD_1213582242930949697188121
2024-08-16T14:59:46
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PO_1227803043032049277083090
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MD_3580205984756795750502668
null
[ "Re_iter2_sc_elastic_B222_dist03_4_3500_1" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9883684717336813160611711
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re16
Re
A
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MD_1213582242930949697188121
2024-08-16T14:48:31
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MD_3894327761601644436733559
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[ "Re_iter2_0_mp-8_elastic_B222_dist03_6_3500_3" ]
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2023-12-01T22:58:16
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CO_9891585804056718678374550
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re17
Re
A
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MD_1213582242930949697188121
2024-08-16T14:30:08
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MD_3894327761601644436733559
null
[ "Re_iter1_0_mp-8_interstitials_HCP2_crowdionB_3500_16" ]
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2023-12-01T22:58:16
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CO_9894102302239578962492823
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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{'source-publication': 'https://doi.org/10.1039/D3DD00046J', 'source-data': 'https://github.com/saidigroup/23-Single-Element-DNPs', 'other': None}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re17
Re
A
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MD_1213582242930949697188121
2024-08-16T15:38:59
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MD_3140906615584827818889926
null
[ "Re_iter0_T3500_0_mp-8_interstitials_HCP2_crowdion_0" ]
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2023-12-01T22:58:16
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CO_9905406463147808254415026
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re32
Re
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MD_1213582242930949697188121
2024-08-16T14:50:09
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PO_1481857411969539379364892
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MD_5276451162749980934843793
null
[ "Re_iter0_T3500_3_mp-8642_elastic_B222_dist03_4_7" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9912434689682939666736753
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re32
Re
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MD_1213582242930949697188121
2024-08-16T15:40:30
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PO_9066068655558721089507986
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MD_2333412701806194962250672
null
[ "Re_iter1_3_mp-8642_elastic_B222_dist03_4_3500_9" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9928383166780832738641478
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re32
Re
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MD_1213582242930949697188121
2024-08-16T15:39:51
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MD_2333412701806194962250672
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2023-12-01T22:58:16
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CO_9932437988386985129462596
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re17
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MD_1213582242930949697188121
2024-08-16T14:35:35
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MD_1951564499902567866502008
null
[ "Re_iter1_0_mp-8_interstitials_HCP2_crowdionB2_875_6" ]
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2023-12-01T22:58:16
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CO_9935175427603205246665537
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re16
Re
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MD_1213582242930949697188121
2024-08-16T15:40:09
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PO_9232663866076482518400113
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MD_3140906615584827818889926
null
[ "Re_iter0_T3500_0_mp-8_elastic_B222_dist03_10_13" ]
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2023-12-01T22:58:16
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CO_9956913321672203082487986
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
Re32
Re
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MD_1213582242930949697188121
2024-08-16T14:45:33
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2023-12-01T22:58:16
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CO_9966186199513405245436372
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*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_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0
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MD_1213582242930949697188121
2024-08-16T14:35:26
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MD_1951564499902567866502008
null
[ "Re_iter1_0_mp-8_interstitials_HCP2_dbB_875_12" ]
[ "DS_gzbvdicu231p_0" ]
2023-12-01T22:58:16
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CO_9992109309381204470816502
23-Single-Element-DNPs_RSCDD_2023-Re
[ "Christopher M. Andolina", "Wissam A. Saidi" ]
Configurations of Re from Andolina & Saidi, 2023. One of 23 minimalist, curated sets of DFT-calculated properties for individual elements for the purpose of providing input to machine learning of deep neural network potentials (DNPs). Each element set contains on average ~4000 structures with 27 atoms per structure. Configuration metadata includes Materials Project ID where available, as well as temperatures at which MD trajectories were calculated.These temperatures correspond to the melting temperature (MT) and 0.25*MT for elements with MT < 2000K, and MT, 0.6*MT and 0.25*MT for elements with MT > 2000K.
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2023-12-01T17:58:30
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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}
10.60732/1e0997f8
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DS_gzbvdicu231p_0
23-Single-Element-DNPs_RSCDD_2023-Re__Andolina-Saidi__DS_gzbvdicu231p_0