Dataset

AlNiTi_CMS_2019



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Name AlNiTi_CMS_2019
Extended ID AlNiTi_CMS_2019_GubaevPodryabinkinHartShapeev__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
Elements Al (29.66%)
Ni (38.25%)
Ti (32.09%)
Number of Data Objects 2,666
Number of Configurations 2,684
Number of Atoms 25,067
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 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
Data Objects
ColabFit ID DS_dtjyh96dypuu_0
Files colabfitspec.json

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