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 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.
- Authors:
-
- Hefei Univ. of Technology (China). School of Electronic Science and Applied Physics
- 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
- Publication Date:
- Research Org.:
- Princeton Univ., NJ (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Key R&D Foundation of China; National Natural Science Foundation of China (NSFC)
- OSTI Identifier:
- 1853193
- Grant/Contract Number:
- SC0019394; 2016YFB0201200; 2016YFB0201203; 11871110
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Frontiers in Chemistry
- Additional Journal Information:
- Journal Volume: 8; Journal ID: ISSN 2296-2646
- Publisher:
- Frontiers Research Foundation
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry; many-body potential energy; deep learning; crystal structure prediction; Al-Mg; alloy
Citation Formats
Wang, Haidi, Zhang, Yuzhi, Zhang, Linfeng, and Wang, Han. Crystal Structure Prediction of Binary Alloys via Deep Potential. United States: N. p., 2020.
Web. doi:10.3389/fchem.2020.589795.
Wang, Haidi, Zhang, Yuzhi, Zhang, Linfeng, & Wang, Han. Crystal Structure Prediction of Binary Alloys via Deep Potential. United States. https://doi.org/10.3389/fchem.2020.589795
Wang, Haidi, Zhang, Yuzhi, Zhang, Linfeng, and Wang, Han. Thu .
"Crystal Structure Prediction of Binary Alloys via Deep Potential". United States. https://doi.org/10.3389/fchem.2020.589795. https://www.osti.gov/servlets/purl/1853193.
@article{osti_1853193,
title = {Crystal Structure Prediction of Binary Alloys via Deep Potential},
author = {Wang, Haidi and Zhang, Yuzhi and Zhang, Linfeng and Wang, Han},
abstractNote = {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.},
doi = {10.3389/fchem.2020.589795},
journal = {Frontiers in Chemistry},
number = ,
volume = 8,
place = {United States},
year = {Thu Nov 26 00:00:00 EST 2020},
month = {Thu Nov 26 00:00:00 EST 2020}
}
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