Electrode-Electrolyte Interfaces Probed by Quantum-Chemical Simulations and Machine Learning for Lithium-Ion Batteries

Understanding electrode-electrolyte interactions and predicting their consequence on structural properties of the interface is crucial for optimizing performance of various energy capture and storage devices, such as electrolyzers, fuel cells, and batteries. For example, the oxidation of organic electrolytes at the cathode surfaces of rechargeable lithium-ion batteries leads to the formation of the electrode-electrolyte interface (EEI) that has high impedance and is partially responsible for the degradation of lithium-ion batteries. Due to the complexity of interfacial processes involving charge transfer and structural transformations, the composition and formation mechanism of such surface layers remains elusive, rendering the rational design of cathode materials rather challenging. Quantum chemical simulations of materials properties have significantly improved our understanding of electrochemical processes, however, due to the lack of physical and reliable descriptors, the community has not been able to understand the interfacial interactions and their relevance to the essential characteristics of lithium-ion batteries, such as safety, energy, and power densities.