Crystal Structure Prediction of Binary Alloys via Deep Potential.

ABSTRACT: 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 format

SUBMITTER: Wang H 

PROVIDER: S-EPMC7732660 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature