(Inter)facing the Bitter Truth: How to Design Better Interfaces in Next-Gen Batteries using Atomistic Simulations Assisted by Machine-Learning

（交互）面对痛苦的真相：如何使用机器学习辅助的原子模拟设计下一代电池中更好的界面

• 批准号: 2886070
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2886070
• 关键词: Inter; facing; Bitter; Truth; How

Lithium-Sulphur batteries (LSBs) are a promising alternative to Li-ion batteries (LIBs) as a next-gen energy storage technology, providing higher theoretical capacity at lower costs. Replacing the conventional liquid electrolytes with solid electrolytes (SE) helps mitigate the major LSB issues like the Li-polysulfide shuttle effect, and safety risks. Current SEs, however, degrade when coupled with a S-cathode, impeding the Li-ion conduction across their interfaces, limiting the battery performance. To design superior SE/S-cathode interfaces, this project focuses on atomistic simulations of the interfacial sulphide conversion chemistry in LSBs utilising state-of-the-art Density Functional Theory and machine learning methods, providing insights that are otherwise elusive to experimental characterisation techniques.

锂硫电池（LSB）是锂离子电池（LIB）的一种有前途的替代品，可作为下一代储能技术，以更低的成本提供更高的理论容量。用固体电解质（SE）取代传统的液体电解质有助于减轻主要的LSB问题，如锂-多硫化物穿梭效应和安全风险。然而，当前SE在与S-阴极耦合时劣化，从而阻碍Li离子在其界面上的传导，限制电池性能。为了设计上级SE/S-阴极界面，该项目利用最先进的密度泛函理论和机器学习方法，重点研究LSB中界面硫化物转化化学的原子模拟，提供实验表征技术难以理解的见解。