Dataset

CuPd_CMS2019


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Name CuPd_CMS2019
Extended ID CuPd_CMS2019__Gubaev-Podryabinkin-Hart-Shapeev__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
DOI 10.60732/1058e01c
https://commons.datacite.org/doi.org/10.60732/1058e01c
https://doi.datacite.org/dois/10.60732%2F1058e01c
https://doi.org/10.60732/1058e01c

Cite as: Gubaev, K., Podryabinkin, E. V., Hart, G. L., and Shapeev, A. V. "CuPd CMS2019." ColabFit, 2023. https://doi.org/10.60732/1058e01c.
For other citation formats, see the DataCite Fabrica page for this dataset.
Calculated Property Types atomic_forces
cauchy_stress
energy
Elements Cu (54.0%)
Pd (46.0%)
Number of Configurations 522
Number of Atoms 2,450
Links https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data
https://doi.org/10.1016/j.commatsci.2018.09.031
Configuration Sets by Name (None)
Configuration Sets by ID (None)
Calculated Properties
ColabFit ID DS_0ry6z1j8mi8c_0
Files colabfitspec.json

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