Machine Learning-Accelerated First-Principles Study of Atomic Configuration and Ionic Diffusion in Li10GeP2S12 Solid Electrolyte
文献类型：期刊论文
| 作者 | Qi, Changlin5,6; Zhou, Yuwei4,6; Yuan XZ(袁晓泽)3,5; Peng Q(彭庆)1,2,3; Yang, Yong4,6; Li, Yongwang4; Wen, Xiaodong4,5,6 |
| 刊名 | MATERIALS
 |
| 出版日期 | 2024-04-01 |
| 卷号 | 17期号:8页码:14 |
| 关键词 | 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 |
| 通讯作者 | Zhou, Yuwei(zhouyuwei_kt@163.com) ; Peng, Qing(pengqing@imech.ac.cn) |
| 英文摘要 | 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. |
| 分类号 | 二类/Q1 |
| WOS关键词 | LITHIUM ; DYNAMICS ; CONDUCTIVITY ; INSIGHTS ; BATTERY ; FAMILY |
| 资助项目 | National Science Fund for Distin-guished Young Scholars of China |
| WOS研究方向 | Chemistry ; Materials Science ; Metallurgy & Metallurgical Engineering ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001211215800001 |
| 资助机构 | National Science Fund for Distin-guished Young Scholars of China |
| 其他责任者 | Zhou, Yuwei ; Peng, Qing |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/95088]  |
| 专题 | 力学研究所_非线性力学国家重点实验室 |
| 作者单位 | 1.Guangdong Aerosp Res Acad, Guangzhou 511458, Peoples R China 2.Univ Chinese Acad Sci, Ctr Mat Sci & Optoelect Engn, Beijing 100049, Peoples R China; 3.Inst Mech, Chinese Acad Sci, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 4.Synfuels China Co Ltd, Natl Energy Ctr Coal Clean Fuels, Beijing 101400, Peoples R China; 5.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China; 6.Inst Coal Chem, Chinese Acad Sci, State Key Lab Coal Convers, Taiyuan 030001, Peoples R China; |
| 推荐引用方式 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. |
| 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. |
入库方式： OAI收割
来源：力学研究所
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