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

锂硫电池(LSB)是一种很有前途的替代锂离子电池(LIB)的新一代储能技术，可以以较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缓解主要的LSB问题，如Li-多硫化物穿梭效应和安全风险。然而，电流SE在与S阴极结合时会退化，阻碍锂离子在其界面上的传导，从而限制电池性能。为了设计优良的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对LSB中的界面硫化物转化化学进行了原子模拟，提供了其他实验表征技术难以获得的见解。

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.