| Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte | |
| Qi, Changlin1,2; Zhou, Yuwei1,3; Yuan XZ(袁晓泽)2,4; Peng Q(彭庆)4,5,6; Yang, Yong1,3; Li, Yongwang3; Wen, Xiaodong1,2,3 | |
| Corresponding Author | Zhou, Yuwei([email protected]) ; Peng, Qing([email protected]) |
| Source Publication | MATERIALS
 |
| 2024-04-01 | |
| Volume | 17Issue:8Pages:14 |
| 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. |
| Keyword | Li10GeP2S12 solid electrolyte first-principles calculation Ewald-summation-based electrostatic energy machine learning- and active-learning-based LAsou method ab initio molecular dynamics |
| DOI | 10.3390/ma17081810 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001211215800001 |
| WOS Keyword | LITHIUM ; DYNAMICS ; CONDUCTIVITY ; INSIGHTS ; BATTERY ; FAMILY |
| WOS Research Area | Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering ; Physics |
| WOS Subject | Chemistry, Physical ; Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering ; Physics, Applied ; Physics, Condensed Matter |
| Funding Project | National Science Fund for Distin-guished Young Scholars of China |
| Funding Organization | National Science Fund for Distin-guished Young Scholars of China |
| Classification | 二类/Q1 |
| Ranking | 1 |
| Contributor | Zhou, Yuwei ; Peng, Qing |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/95089 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Inst Coal Chem, Chinese Acad Sci, State Key Lab Coal Convers, Taiyuan 030001, Peoples R China; 2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China; 3.Synfuels China Co Ltd, Natl Energy Ctr Coal Clean Fuels, Beijing 101400, Peoples R China; 4.Inst Mech, Chinese Acad Sci, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 5.Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China; 6.Guangdong Aerosp Res Acad, Guangzhou 511458, Peoples R China |
| Recommended Citation GB/T 7714 | Qi, Changlin,Zhou, Yuwei,Yuan XZ,et al. Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte[J]. MATERIALS,2024,17,8,:14.Rp_Au:Zhou, Yuwei, Peng, Qing |
| APA | Qi, Changlin.,Zhou, Yuwei.,袁晓泽.,彭庆.,Yang, Yong.,...&Wen, Xiaodong.(2024).Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte.MATERIALS,17(8),14. |
| MLA | Qi, Changlin,et al."Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte".MATERIALS 17.8(2024):14. |
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