Accelerating the Discovery of Transition Metal Borides by Machine Learning on Small Data Sets

ACS Appl Mater Interfaces. 2023 Jun 21;15(24):29278-29286. doi: 10.1021/acsami.3c03657. Epub 2023 Jun 6.

Abstract

Accurate and efficient prediction of the stability and structure-stability relationship is important to discover materials; however, it requires tremendous efforts via traditional trial-and-error schemes. Here, we presented a small-data set machine learning (ML) method to accelerate the discovery of promising ternary transition metal boride (MAB) candidates. Based on data sets obtained by ab initio calculations, we developed three robust neural networks to predict the decomposition energy (ΔHd) and assess the thermodynamic stability of 212-typed MABs (M2AB2). The quantitative relation between ΔHd and stability was unraveled by several composition-and-structure descriptors. Three hexagonal M2AB2, i.e., Nb2PB2, Nb2AsB2, and Zr2SB2, were discovered to be stable with negative ΔHd, and 75 metastable MABs were identified with ΔHd less than 70 meV/atom. Finally, the dynamical stability and mechanical properties of MABs were investigated by ab initio calculations, whose results further verified the reliability of our ML models. This work provided a ML approach on small data sets to accelerate the discovery of compounds and expanded the MAB phase family to VA and VIA groups.

Keywords: MAB phases; ab initio calculations; machine learning; small data sets; stability.