Authors: Kwangnam Kim | Suyue Yuan | Brandon C. Wood | Liwen F. Wan
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Interfacial Degradation in All-Solid-State Batteries in Atomic Scale Using Machine Learning Force Fields As a promising future energy storage solution, all-solid-state battery (ASSB) technologies with less flammable and non-volatile solid electrolytes (SEs) have attracted great attention in recent decades for their potential to achieve higher energy densities and improved safety compared to conventional Li-ion batteries. Nevertheless, challenges remain for practical deployment due to electro-chemo-mechanical instabilities at solid-solid interfaces. These interfaces, which include homogeneous/internal interfaces such as grain boundaries (GBs) and heterogeneous/external interfaces between SE and electrode materials, can impede Li-ion transport, reduce electrochemically active sites, deteriorate performance, and eventually lead to cell failure. To resolve these issues, fundamental understanding of the structure-property relationships at the interfaces, which govern the electrochemical performance of ASSBs, is required. In this talk, we will present our recent results utilizing large-scale molecular dynamics simulations with validated machine-learning force fields to explore interfacial degradations in ASSBs directly in atomic scale and reveal the fundamental mechanisms of physicochemical phenomena observed in experiments. First, we surveyed different geometries and compositions at the garnet Li 7 La 3 Zr 2 O 12 (LLZO) SE/LiCoO 2 (LCO) cathode interfaces simulating co-sintering environment at high temperatures, which suggest the propensities of chemical degradation depending on the interfacial chemistry as well as the mechanisms for the formation of Co-rich phases at LLZO GBs. We also explored the mechanisms of dopant segregation in Al/Ta-doped LLZO and elucidate their effects on the properties of materials and implications to the battery performance. At last, we will address the micro-crack propagation behavior and mechanical responses in LLZO. In summary, our results reveal how atomic details of the dynamically evolving interfaces dictate the performance of ASSBs and provide guidance for processing and interface design to achieve desired performance. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract number DEAC52-07NA27344. Authors acknowledge funding support from the Vehicle Technologies Office, Office of Energy Efficiency and Renewable Energy, U.S. Department of Energy and computational resource support from the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Additional computational resources were sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy located at the National Renewable Energy Laboratory and the Computing Grand Challenge program from Lawrence Livermore National Laboratory. Atomic units ; Degradation (telecommunications) ; Battery (electricity) ; Cathode ; Dopant ; Flammable liquid ; Electrode ; Grain boundary Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research