Revealing the Formation Energy–Exfoliation Energy–Structure Correlation of MAB Phases Using Machine Learning and DFT

Abstract

MAB phases became popular as ultrahigh-temperature materials with high damage tolerance and excellent electrical conductivity. MAB is used to exfoliate two-dimensional (2D) transition metal borides (MBenes), which are promising materials for developing next-generation nanodevices. In this report, we explore the correlation between the formation energy, exfoliation energy, and structural factors of MAB phases with orthorhombic and hexagonal crystal symmetries using density functional theory (DFT) and machine learning. For this, we developed three different machine learning models based on the support vector machine, deep neural network, and random forest regressor to study the stability of the MAB phases by calculating their formation energies. Our support vector machine and deep neural network models are capable of predicting the formation energies with mean absolute errors less than 0.1 eV/atom. MAB phases with the chemical formulas, MAB, M2AB2, and M3AB4, where M = Nb, Mn, Ti, W, V, Sc, Cr, Hf, Mo, Zr, Ta, and Fe, and A= group III-A elements (Al, Ga, In and Tl), were investigated to find out the formation energy and their structure correlation. We demonstrated that the stability of a MAB phase for a given transition metal decreases when the A element changes from Al to Tl. DFT revealed that M-A and M-B bond strength strongly correlates with the stability of MAB phases. In addition, the exfoliation possibility of 2D MBenes becomes higher when the A element changes from Al to Tl due to the weakening of M-A and M-B bonds.