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Name 23-Single-Element-DNPs_RSCDD_2023-Pt
Extended ID 23-Single-Element-DNPs_RSCDD_2023-Pt_AndolinaSaidi__DS_0zgz34a90a6i_0
Description Configurations of Pt 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 10.60732/49b97320

Cite as: Andolina, C. M., and Saidi, W. A. "23-Single-Element-DNPs RSCDD 2023-Pt." ColabFit, 2023.
For other citation formats, see the DataCite Fabrica page for this dataset.
Elements Pt (100.0%)
Number of Data Objects 2,609
Number of Configurations 2,609
Number of Atoms 62,152
Configuration Sets by Name 23-Single-Element-DNPs_RSCDD_2023_Pt_initial — Initial training configurations of Pt from 23-Single-Element-DNPs_RSCDD_2023
23-Single-Element-DNPs_RSCDD_2023_Pt_adaptive — Adaptive training configurations of Pt from 23-Single-Element-DNPs_RSCDD_2023
Configuration Sets by ID CS_enq31jtfs80p_0
Data Objects
ColabFit ID DS_0zgz34a90a6i_0
Files colabfitspec.json

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