AI 中文总结
本研究开发结合通用极化力场与机器学习力场的模拟方法，预测锂金属表面界面相化学，揭示电解质等与SEI性质的分子关联，为AI驱动电解质发现提供基础。
AI 中文摘要
界面相仍是先进电池中最不被理解的组分，尽管其性质决定了新型电池化学能否按设计性能运行，但由于缺乏界面相形成过程的原子级知识，从未有可靠方法预测新型电解质体系会产生何种界面相。本研究尝试开发一种可通用预测锂金属表面形成的界面相化学的模拟方法，使电解质工程不再需要冗长的爱迪生式试错法。通过结合可转移通用极化力场与通用机器学习力场，我们模拟了化学性质多样的电解质配方下的界面相化学，成功复现实验观测：氟化溶剂促进富LiF界面相形成，而常规碳酸盐基电解质产生更多有机来源的界面相。通过直接捕获这些界面相化学背后的自发界面反应，我们的模拟建立了电解质化学、盐浓度、分解路径与SEI性质之间的分子级关联，为通用且高通量的界面相预测模拟开辟了路径，这是AI驱动电解质发现的基础。
英文摘要
Interphases remain the least understood components in advanced batteries. Although their properties dictate whether a new battery chemistry could perform as designed, there has never been a reliable way to predict what an interphase could arise from a new electrolyte system due to the absence of atomistic level knowledge about interphasial formation process. In this work, we attempt to develop a simulation method that can universally predict interphasial chemistries formed on Li metal surface, so that the electrolyte engineering would no longer need lengthy Edisonian approaches. By combining a transferable universal polarizable force field and a universal machine learning force field, we simulate interphasial chemistry across chemically diverse electrolyte formulations, and successfully replicate the experimental observation that fluorinated solvents promote the formation of LiF-rich interphases, whereas interphases of more organic origin arise from conventional carbonate-based electrolytes. By directly capturing these spontaneous interfacial reactions behind these interphasial chemistries, our simulations establish molecular-level relationships between electrolyte chemistry, salt concentration, decomposition pathways, and SEI properties, and opens a route toward universal and high-throughput predictive simulation of interphases that is the foundation for AI-driven electrolyte discoveries.
Comments16 pages, 4 tables, 5 figures
