通过机器学习力场模拟,了解硫化玻璃电解质结构和离子导电性
Rui Zhou1, Kun Luo1, Steve W Martin1
1Department of Materials Science and Engineering, Iowa State University, Ames, Iowa 50011, United States.
ACS applied materials & interfaces
|April 3, 2024
概括
一个新的机器学习力场 (ML-FF) 能够对硫化物基玻璃状固体电解质 (GSEs) 进行详细研究. 这个工具有助于理解先进的全固态电池 (ASSB) 的结构属性关系.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算材料科学科学 计算材料科学
- 电化学 电化学 电化学
背景情况:
- 基于硫化物的固体电解质 (SE) 对于全固态电池 (ASSB) 是非常重要的,因为其具有高离子导电性和机械稳定性.
- 具有混合Si和P成份的玻璃SE (GSEs) 提供了有前途的合成路径和树突抑制,但它们的复杂结构阻碍了对属性的理解.
- 对SE结构和属性的准确建模对于设计下一代ASSB至关重要.
研究的目的:
- 开发一种机器学习力场 (ML-FF) 来准确模拟基于硫化的GSEs.
- 研究各种GSE组合物的结构性质关系,包括机械性质和离子导电性.
- 阐明这些电解质中控制离子扩散的机制.
主要方法:
- 开发一种新的机器学习力场 (ML-FF),专门用于基于硫化的GSEs.
- 使用ML-FF进行分子动力学 (MD) 模拟,以探索二进制 (Li2S-SiS2,Li2S-P2S5) 和三进制 (Li2S-SiS2-P2S5) 组合.
- 对实验数据和密度函数理论 (DFT) 对密度,弹性模量和结构因子的计算进行模拟结果的验证.
主要成果:
- ML-FF准确地复制了关键材料特性 (密度,弹性模量,辐射分布函数,中子结构因子).
- 为Si和P确定了不同的局部环境,Si在Li2S-SiS2中偏爱边缘共享,并在三元玻璃中混合共享.
- 离子导电性因组成而异,75Li2S-25P2S5显示最高 (3.6 mS/cm),50Li2S-50SiS2显示最低 (2.1 mS/cm) 在300 K.
- 三元玻璃中的离子扩散与SiS4和PS4四面体的旋转动态相关.
结论:
- 开发的ML-FF是模拟和理解基于硫化物的GSEs的强大工具.
- 该研究提供了对这些材料离子导电性的结构基础的见解.
- 这项工作促进了ASSB的SE的合理设计和优化.
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