1. Prediction of stable Li-Sn compounds: boosting ab initio searches with neural network potentials
- Author
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Saba Kharabadze, Aidan Thorn, Ekaterina A. Koulakova, and Aleksey N. Kolmogorov
- Subjects
Materials of engineering and construction. Mechanics of materials ,TA401-492 ,Computer software ,QA76.75-76.765 - Abstract
Abstract The Li-Sn binary system has been the focus of extensive research because it features Li-rich alloys with potential applications as battery anodes. Our present re-examination of the binary system with a combination of machine learning and ab initio methods has allowed us to screen a vast configuration space and uncover a number of overlooked thermodynamically stable alloys. At ambient pressure, our evolutionary searches identified an additional stable Li3Sn phase with a large BCC-based hR48 structure and a possible high-T LiSn4 ground state. By building a simple model for the observed and predicted Li-Sn BCC alloys we constructed an even larger viable hR75 structure at an exotic 19:6 stoichiometry. At 20 GPa, low-symmetry 11:2, 5:1, and 9:2 phases found with our global searches destabilize previously proposed phases with high Li content. The findings showcase the appreciable promise machine-learning interatomic potentials hold for accelerating ab initio prediction of complex materials.
- Published
- 2022
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