Buried No longer: recent computational advances in explicit interfacial modeling of lithium-based all-solid-state battery materials
All-solid-state ceramic batteries with Li metal anodes promise substantial gains in energy density, owing to the metal’s high theoretical capacity and low reduction potential, as well as enhanced safety. However, realizing these benefits requires optimization of buried grain boundaries and interfaces within and between a cell’s bulk components, through intentionally designed interfaces, targeted grain boundary engineering, rational synthesis strategies, and beyond. In this Review, we examine recent atomistic simulations that provide insights into such solutions by elucidating ion transport, electron transfer, and chemical reactivity in solid state electrolyte grain boundaries, cathode | electrolyte interfaces, cathode | cathode grain boundaries, and electrolyte interfaces in anode-free solid-state batteries. We also discuss the advantages and limitations of the various computational methods applied. Lastly, we highlight universal machine learning potentials, challenging datasets, and opportunities for tighter integration with experiments, all of which broaden the scope of modeling. These developments enable unprecedented large-scale simulations of buried solid | solid interfaces, potentially accelerating progress to understand and improve ASSB performance in silico .
Authors
- Julia H. Yang (ORCID: https://orcid.org/0000-0002-5713-2288)
- Amanda Whai Shin Ooi
- Xinqiang Rao
Institutions
- Georgia Institute of Technology (US)
- KLA (United States) (US)
Publication Details
- Journal
- Frontiers in Energy Research
- Published
- 2025-08-13
- DOI
- https://doi.org/10.3389/fenrg.2025.1621807
- Citations
- 2
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- Georgia Institute of Technology