Crystal Structure Prediction of Binary Alloys via Deep Potential - Wang, Haidi; Zhang, Yuzhi; Zhang, Linfeng; Wang, Han (Frontiers in Chemistry, 2020)
Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this article, we employ a previously developed Deep Potential model to predict the intermetallic compound of the Al-Mg system, and find 6 meta-stable phases with negative or nearly zero formation energy. In particular, $\chem{Mg_{12}Al_{8}}$ shows excellent ductility and $\chem{Mg_5Al_{27}}$ has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with {\it ab initio} accuracy can be dramatically reduced.