Crystal Structure Prediction of Binary Alloys via Deep Potential

Frontiers in Chemistry

Yuzhi Zhang¹  Han Wang²  Linfeng Zhang³  Haidi Wang⁴ 

[1] Beijing Institute of Big Data Research, Peking University, Beijing, China;Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, China;Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, United States;School of Electronic Science and Applied Physics, Hefei University of Technology, Hefei, China;Yuanpei College of Peking University, Beijing, China;

关键词: many-body potential energy;    deep learning;    crystal structure prediction;    Al-Mg;    alloy;   

DOI  :  10.3389/fchem.2020.589795

来源: DOAJ

【 摘 要 】

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 paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg12Al8 shows excellent ductility and Mg5Al27 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 ab initio accuracy can be dramatically reduced.

【 授权许可】

Unknown