Computational material science is crucial to establish links between complex phenomena occurring at the atomic scale and macroscopic observations of functional materials. For example, all-solid-state batteries utilizing solid-electrolyte “membranes” separating the distinct chemistries of the electrode materials are a safer alternative to current liquid electrolyte systems, but their solid-solid “buried” interfaces remain poorly understood. In this talk, I will showcase the power of machine-learning-driven simulations to inform the complex reaction mechanisms that take place at these interfaces. I will also demonstrate that first-principles methods can guide the design of Natrium Super Ionic CONductors (NaSICONs) electrodes and electrolytes with superior energy densities and improved properties of ion transport.