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Qi C, Zhou Y, Yuan X, Peng Q, Yang Y, Li Y, Wen X. Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li 10GeP 2S 12 Solid Electrolyte. Materials (Basel) 2024; 17:1810. [PMID: 38673167 PMCID: PMC11051406 DOI: 10.3390/ma17081810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/07/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024]
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.
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Affiliation(s)
- Changlin Qi
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
| | - Yuwei Zhou
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
| | - Xiaoze Yuan
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
| | - Qing Peng
- State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Guangdong Aerospace Research Academy, Guangzhou 511458, China
| | - Yong Yang
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
| | - Yongwang Li
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
| | - Xiaodong Wen
- State Key Laboratory of Coal Conversion, Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China (X.W.)
- University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
- National Energy Center for Coal to Clean Fuels, Synfuels China Co., Ltd., Huairou District, Beijing 101400, China
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