GeoDict-Enabled Digital Twin Modeling for Microstructure-Based Battery Analysis and Design

Rechargeable battery performance is governed not only by the intrinsic properties of constituent materials but also by their three-dimensional microstructures, which determine transport pathways, interfacial contact, reaction heterogeneity, and mechanical stability. Digital twin modeling provides a powerful framework for quantitatively linking these microstructural features to cell performance and for guiding the design of next-generation battery components.

In this presentation, we introduce GeoDict-enabled digital twin approaches for the analysis and design of three representative battery systems: composite electrodes for all-solid-state batteries, porous separators, and dry-processed electrodes. For all-solid-state battery electrodes, reconstructed and virtual microstructures are used to quantify active material–solid electrolyte contact, ionic and electronic transport pathways, tortuosity, and local reaction distributions. These analyses clarify how particle arrangement, component fraction, and interfacial connectivity affect effective transport properties and electrochemical utilization. For battery separators, stochastic structure generation is combined with transport simulations to establish quantitative relationships among porosity, pore size, fiber morphology, anisotropy, and effective ionic conductivity. For dry-processed electrodes, digital microstructures are employed to evaluate particle–binder–conductive additive networks, pore connectivity, and the structural factors governing charge transport in highly loaded electrodes.

Together, these examples demonstrate that GeoDict-based digital twins can extend battery research beyond conventional material-level characterization by enabling microstructure-resolved analysis, performance prediction, and virtual design. The presentation will also discuss future directions, including manufacturing-aware structure generation, electrochemical–mechanical coupling, image-assisted reconstruction, and AI-enabled inverse design for accelerated optimization of battery architectures.