Dataset

23-Single-Element-DNPs_RSCDD_2023-Cu



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Name 23-Single-Element-DNPs_RSCDD_2023-Cu
Extended ID 23-Single-Element-DNPs_RSCDD_2023-Cu_AndolinaSaidi__DS_dc3o40aou2le_0
Description 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.
Authors Christopher M. Andolina
Wissam A. Saidi
DOI 10.60732/e0a72dd8
https://commons.datacite.org/doi.org/10.60732/e0a72dd8
https://doi.datacite.org/dois/10.60732%2Fe0a72dd8
https://doi.org/10.60732/e0a72dd8

Cite as: Andolina, C. M., and Saidi, W. A. "23-Single-Element-DNPs RSCDD 2023-Cu." ColabFit, 2023. https://doi.org/10.60732/e0a72dd8.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements Cu (100.0%)
Number of Data Objects 3,366
Number of Configurations 3,366
Number of Atoms 96,568
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_Cu_initial — Initial training configurations of Cu from 23-Single-Element-DNPs_RSCDD_2023
23-Single-Element-DNPs_RSCDD_2023_Cu_adaptive — Adaptive training configurations of Cu from 23-Single-Element-DNPs_RSCDD_2023
Configuration Sets by ID CS_4tmvc6p0omdc_0
CS_su57p14kic32_0
Data Objects
ColabFit ID DS_dc3o40aou2le_0
Files colabfitspec.json

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