Dataset
AlNiTi_CMS_2019
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| Name | AlNiTi_CMS_2019 |
|---|---|
| Extended ID | AlNiTi_CMS_2019__Gubaev-Podryabinkin-Hart-Shapeev__DS_dtjyh96dypuu_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 about 375,000 binary and ternary structures, enumerating all possible unit cells with different symmetries (BCC, FCC, and HCP) and different number of atoms. |
| Authors |
Konstantin Gubaev Evgeny V. Podryabinkin Gus L.W. Hart Alexander V. Shapeev |
| DOI |
10.60732/7b56ca82
https://commons.datacite.org/doi.org/10.60732/7b56ca82 https://doi.datacite.org/dois/10.60732%2F7b56ca82 https://doi.org/10.60732/7b56ca82 Cite as: Gubaev, K., Podryabinkin, E. V., Hart, G. L., and Shapeev, A. V. "AlNiTi CMS 2019." ColabFit, 2023. https://doi.org/10.60732/7b56ca82. For other citation formats, see the DataCite Fabrica page for this dataset. |
| Calculated Property Types |
atomic_forces cauchy_stress energy |
| Elements |
Al (29.66%) Ni (38.25%) Ti (32.09%) |
| Number of Configurations | 2,684 |
| Number of Atoms | 25,067 |
| 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 |
1st_stage — Configurations used in the first stage of training 2nd_stage — Configurations used in the second stage of training |
| Configuration Sets by ID |
CS_a26ebt2g57et_0 CS_x98wfu88su6y_0 |
| Calculated Properties | |
| ColabFit ID | DS_dtjyh96dypuu_0 |
| Files | colabfitspec.json |
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