Dataset

CuPd_CMS2019



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Name CuPd_CMS2019
Extended ID CuPd_CMS2019_GubaevPodryabinkinHartShapeev__DS_0ry6z1j8mi8c_0
Description This dataset was generated using the following active learning scheme: 1) candidate structures were relaxed by a partially-trained MTP model, 2) structures for which the MTP had to perform extrapolation were passed to DFT to be re-computed, 3) the MTP was retrained, including the structures that were re-computed with DFT, 4) steps 1-3 were repeated until the MTP no longer extrapolated on any of the original candidate structures. The original candidate structures for this dataset included 40,000 unrelaxed configurations with BCC, FCC, and HCP lattices.
Authors Konstantin Gubaev
Evgeny V. Podryabinkin
Gus L.W. Hart
Alexander V. Shapeev
Elements Cu (54.0%)
Pd (46.0%)
Number of Data Objects 522
Number of Configurations 522
Number of Atoms 2,450
Links https://doi.org/10.1016/j.commatsci.2018.09.031
https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Data Objects
ColabFit ID DS_0ry6z1j8mi8c_0
Files colabfitspec.json

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