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Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte

Authors :
Changlin Qi
Yuwei Zhou
Xiaoze Yuan
Qing Peng
Yong Yang
Yongwang Li
Xiaodong Wen
Source :
Materials, Vol 17, Iss 8, p 1810 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The solid electrolyte Li10GeP2S12 (LGPS) plays a crucial role in the development of all-solid-state batteries and has been widely studied both experimentally and theoretically. The properties of solid electrolytes, such as thermodynamic stability, conductivity, band gap, and more, are closely related to their ground-state structures. However, the presence of site-disordered co-occupancy of Ge/P and defective fractional occupancy of lithium ions results in an exceptionally large number of possible atomic configurations (structures). Currently, the electrostatic energy criterion is widely used to screen favorable candidates and reduce computational costs in first-principles calculations. In this study, we employ the machine learning- and active-learning-based LAsou method, in combination with first-principles calculations, to efficiently predict the most stable configuration of LGPS as reported in the literature. Then, we investigate the diffusion properties of Li ions within the temperature range of 500–900 K using ab initio molecular dynamics. The results demonstrate that the atomic configurations with different skeletons and Li ion distributions significantly affect the Li ions’ diffusion. Moreover, the results also suggest that the LAsou method is valuable for refining experimental crystal structures, accelerating theoretical calculations, and facilitating the design of new solid electrolyte materials in the future.

Details

Language :
English
ISSN :
19961944
Volume :
17
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Materials
Publication Type :
Academic Journal
Accession number :
edsdoj.1678c3c9f3574e1fa77785b41f5fd9b4
Document Type :
article
Full Text :
https://doi.org/10.3390/ma17081810