Machine Learning-Guided Discovery of Ternary Compounds Containing La, P, and Group 14 Elements
Huaijun Sun, Chao Zhang, Weiyi Xia, Ling Tang, Renhai Wang, Georgiy Akopov, Nethmi W. Hewage, Kai‐Ming Ho, Kirill Kovnir, Cai‐Zhuang Wang
Abstract
We integrate a deep machine learning (ML) method with first-principles calculations to efficiently search for the energetically favorable ternary compounds. Using La–Si–P as a prototype system, we demonstrate that ML-guided first-principles calculations can efficiently explore crystal structures and their relative energetic stabilities, thus greatly accelerate the pace of material discovery. A number of new La–Si–P ternary compounds with formation energies less than 30 meV/atom above the known ternary convex hull are discovered. Among them, the formation energies of La5SiP3 and La2SiP phases are only 2 and 10 meV/atom, respectively, above the convex hull. These two compounds are dynamically stable with no imaginary phonon modes. Moreover, by replacing Si with heavier-group 14 elements in the eight lowest-energy La–Si–P structures from our ML-guided predictions, a number of low-energy La–X–P phases (X = Ge, Sn, Pb) are predicted.