Dataset

23-Single-Element-DNPs_RSCDD_2023-Ge


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Name 23-Single-Element-DNPs_RSCDD_2023-Ge
Extended ID 23-Single-Element-DNPs_RSCDD_2023-Ge__Andolina-Saidi__DS_90fnsmavv1am_0
Description Configurations of Ge 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 10.60732/33767c4f
https://commons.datacite.org/doi.org/10.60732/33767c4f
https://doi.datacite.org/dois/10.60732%2F33767c4f
https://doi.org/10.60732/33767c4f

Cite as: Andolina, C. M., and Saidi, W. A. "23-Single-Element-DNPs RSCDD 2023-Ge." ColabFit, 2023. https://doi.org/10.60732/33767c4f.
For other citation formats, see the DataCite Fabrica page for this dataset.
Calculated Property Types atomic_forces
cauchy_stress
energy
Elements Ge (100.0%)
Number of Configurations 2,895
Number of Atoms 195,270
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_Ge_initial — Initial training configurations of Ge from 23-Single-Element-DNPs_RSCDD_2023
23-Single-Element-DNPs_RSCDD_2023_Ge_adaptive — Adaptive training configurations of Ge from 23-Single-Element-DNPs_RSCDD_2023
Configuration Sets by ID CS_64xb4nnl8lqo_0
CS_dprrl3j404ak_0
Calculated Properties
ColabFit ID DS_90fnsmavv1am_0
Files colabfitspec.json

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