机器学习原子势在锂金属负极中的应用  

Analysis of Li Metal Anode by Machine Learning Potential

作　　者：赖根明 焦君宇 蒋耀 郑家新 欧阳楚英 LAI Genming;JIAO Junyu;JIANG Yao;ZHENG Jiaxin;OUYANG Chuying(School of Advanced Materials,Peking University,Shenzhen Graduate School,Shenzhen 518055,Guangdong,China;Fujian science&technology innovation laboratory for energy devices of China(21C-LAB),Ningde 352100,Fujian,China)

机构地区：[1]北京大学深圳研究生院新材料学院,广东深圳518055 [2]宁德时代21C创新实验室,福建宁德352100

出　　处：《硅酸盐学报》2023年第2期469-475,共7页Journal of The Chinese Ceramic Society

基　　金：国家自然科学基金(52272180,12174162,51962010);宁德时代创新实验室21C-LAB、IER基金2021(IERF202104)。

摘　　要：锂金属是下一代二次电池的理想负极材料。然而,锂枝晶生长存在安全隐患,并导致电池Coulombic效率低,这严重制约了锂二次电池的商业应用。目前,人们对锂的沉积生长机制在原子尺度上还了解甚少,同时对锂枝晶的成因也众说纷纭。近年来,机器学习在计算材料学中的应用使得许多以前无法实现的进步成为可能,本文综述了机器学习原子势在锂金属负极研究中的应用。Li metal is regarded as an ideal anode for the next-generation secondary batteries.However,the growth of Li-dendrite results in a low coulomb efficiency,thus restricting the commercial application of Li secondary batteries.The mechanism of Li deposition and growth in an atomic scale is still unclear,and there are different opinions about the origin of Li dendrite.Recent work has dealt with the application of machine learning in computational materials science.This review represented the applications of machine learning potential for the study of Limetal anode.

关 键 词：锂金属负极 机器学习 分子动力学 计算模拟 

分 类 号：O646[理学—物理化学]