Dataset
23-Single-Element-DNPs_RSCDD_2023-Mo
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| Name | 23-Single-Element-DNPs_RSCDD_2023-Mo |
|---|---|
| Extended ID | 23-Single-Element-DNPs_RSCDD_2023-Mo__Andolina-Saidi__DS_bjkftn0ug9r3_0 |
| 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. |
| Authors |
Christopher M. Andolina Wissam A. Saidi |
| DOI |
10.60732/b6ece7fd
https://commons.datacite.org/doi.org/10.60732/b6ece7fd https://doi.datacite.org/dois/10.60732%2Fb6ece7fd https://doi.org/10.60732/b6ece7fd Cite as: Andolina, C. M., and Saidi, W. A. "23-Single-Element-DNPs RSCDD 2023-Mo." ColabFit, 2023. https://doi.org/10.60732/b6ece7fd. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
Mo (100.0%) |
| Number of Configurations | 3,718 |
| Number of Atoms | 66,612 |
| Links |
https://github.com/saidigroup/23-Single-Element-DNPs https://doi.org/10.1039/D3DD00046J |
| Configuration Sets by Name |
23-Single-Element-DNPs_RSCDD_2023_Mo_initial — Initial training configurations of Mo from 23-Single-Element-DNPs_RSCDD_2023 23-Single-Element-DNPs_RSCDD_2023_Mo_adaptive — Adaptive training configurations of Mo from 23-Single-Element-DNPs_RSCDD_2023 |
| Configuration Sets by ID |
CS_76hrj9n9pbqa_0 CS_d6380tncn94p_0 |
| Calculated Properties | |
| ColabFit ID | DS_bjkftn0ug9r3_0 |
| Files | colabfitspec.json |
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