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 Calculated Property Types: atomic_forces cauchy_stress energy Elements: Cu (54.0%) Pd (46.0%) Methods: DFT-undefined Software: VASP Number of Configurations: 522 Number of Atoms: 2,450 Publication Link: https://doi.org/10.1016/j.commatsci.2018.09.031 Data Source Link: https://gitlab.com/kgubaev/accelerating-high-throughput-searches-for-new-alloys-with-active-learning-data