Dataset
CuPd_CMS2019
Download Dataset XYZ file
| 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 |
| 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. |
| Elements |
Cu (54.0%) Pd (46.0%) |
| Number of Data Objects | 522 |
| 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) |
| Data Objects | |
| ColabFit ID | DS_0ry6z1j8mi8c_0 |
| Files | colabfitspec.json |
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