Data-integrated scale bridging for all-solid state batteries: micro- to mesoscale

Major progress in battery technology is a key to the decarbonization of industry and everyday life. For example, charging rates and battery capacities have been improved in modern lithium ion batteries (LIB). In order to enhance capacity, operational safety and charging, All-Solid-State LIB (ASSLIB) seem promising. In PN 3-1 (II) we provide a coupled multiphysical multiscale technique for the Li ion diffusion and the mechanical behavior within the solid-state electrolyte of ASSLIBs. We resolve the polycrystalline structure using anisotropic material models and we consider diffusion along grain boundaries (GB) with diffusion, temperature, and deformation being strongly coupled. We adaptively explore relevant parameter sets for consideration in challenging atomistic scale simulations by PN 3-10 (II) (Prof. Grabowski) where state-of-the-art machine learning interatomic potentials and ab initio calculations are employed. From this liaison we grow data-integrated material models for the crystal and the GB that evolve gradually with data availability. By teaming up with PN 3-10 (II) we will enable new insights regarding grain size effects, lithium ion diffusion and preferable mechanical constraints (cell pressure) which improve the cell behavior and, thereby, can contribute to the sustainable decarbonization.