Crystal Structure Prediction of Binary Alloys via Deep Potential

Wang, Haidi; Zhang, Yuzhi; Zhang, Linfeng; Wang, Han

doi:10.3389/fchem.2020.589795

Crystal Structure Prediction of Binary Alloys via Deep Potential

Journal Article · Thu Nov 26 00:00:00 EST 2020 · Frontiers in Chemistry

DOI:https://doi.org/10.3389/fchem.2020.589795· OSTI ID:1853193

Wang, Haidi ^[1]; Zhang, Yuzhi ^[2]; Zhang, Linfeng ^[3]; Wang, Han ^[4]

Hefei Univ. of Technology (China). School of Electronic Science and Applied Physics; Princeton Univ., NJ (United States)
Peking Univ., Beijing (China). Yuanpei College; Peking Univ., Beijing (China). Beijing Inst. of Big Data Research
Princeton Univ., NJ (United States). Program in Applied and Computational Mathematics
Inst. of Applied Physics and Computational Mathematics, Beijing (China). Lab. of Computational Physics

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.

View Accepted Manuscript (DOE)

Research Organization:: Princeton Univ., NJ (United States)

Sponsoring Organization:: National Key R&D Foundation of China; National Natural Science Foundation of China (NSFC); USDOE Office of Science (SC)

Grant/Contract Number:: SC0019394

OSTI ID:: 1853193

Journal Information:: Frontiers in Chemistry, Journal Name: Frontiers in Chemistry Vol. 8; ISSN 2296-2646

Publisher:: Frontiers Research FoundationCopyright Statement

Country of Publication:: United States

Language:: English

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