Ion transport kinetics critically define the performance of a wide variety of emerging energy storage solutions. For solid-state devices in particular, ion transport can be heavily influenced by interfaces between different phases or grains, which appear unavoidably from processing or cycling and influence transport in often-problematic ways. With this INCITE project, the researchers aim to integrate a validated multiscale modeling framework to study ion transport kinetics at complex interfaces in solid-state battery and hydrogen storage systems.

To achieve their goal, this team addresses three main objectives: 1) use large-scale quantum simulations to assemble training data for the development of machine-learning force fields based on local ion environments within solids, 2) apply the machine-learning force fields and advanced sampling techniques to compute ion diffusion across a wider variety of configurations, and 3) evaluate the dependence of ion transport on microstructures using mesoscale models. The project also leverages a suite of novel graph-theoretic tools to characterize atomic configurations of disordered interfaces and correlate local arrangements to key properties.

The multiscale simulations are being used to probe the relationship between physicochemical interface properties and ion transport kinetics, guiding rational engineering strategies for improving performance of advanced materials for grid and vehicular energy storage. This project renewal builds upon the team’s previous efforts in simulating thermodynamic and kinetic aspects of hydrogen transport in composite structures for hydrogen storage, as well as lithium transport in a variety of polycrystalline solid-state battery materials.