Integration of experimental insights and computational modeling for all-solid-state batteries

Abstract

All-solid-state batteries (ASSBs) promise superior safety, energy density, and cycle life, but their deployment is hindered by material performance and structural degradation at multiple length scales. This review focuses on how density functional theory (DFT) and allied computational approaches-from ab initio molecular dynamics (AIMD) to multiscale and machine learning (ML) methods to Multiphysics simulations-can systematically reveal the structure-property relationships and evolution mechanisms that control ASSB performance.Recent DFT studies on solid electrolytes, electrodes, and interfaces are discussed, highlighting key metrics such as thermodynamic and electrochemical stability windows, ion-migration barriers, and defect-formation energetics. Next, it is shown how multiscale workflows (AIMD, kinetic Monte Carlo, phase-field, and COMSOL), ML-accelerated screening, and continuum multiphysics modeling (e.g., finite element/pseudo-two-dimensional methods) translate atomistic insights into device-scale predictions for transport, interfacial reactions, mechanical stress, and degradation. For each topic, the successes, persistent gaps, and best-practice strategies for parameter transfer and uncertainty quantification are identified include each halide, sulfide, and oxide-based solid electrolytes (SEs). Actionable research directions to accelerate the design of robust, manufacturable ASSBs are proposed, such as the process of integrated DFT→ML→multiscale simulation→continuum, interfacial engineering guidelines, and manufacturability-aware screening. The aims are bridging theory and practice and providing a roadmap for computationally driven, experimentally validated development of next-generation ASSBs.