Understanding Chemical Evolution at Interfaces in Solid-State Batteries Using Machine Learning Force Fields

Solid-state batteries (SSBs) are next-generation energy storage technologies with improved safety and potentially higher energy densities compared to conventional Li-ion batteries, which is enabled by using fast ion-conducting solid electrolytes (SEs). However, practical applications of SSBs are hindered by the electro-chemo-mechanical instabilities at grain boundaries (GBs; i.e., internal interfaces) as well as external interfaces between SEs and electrodes, which deteriorates Li transport and the chemical and mechanical integrity of the cell. To resolve these issues, fundamental understanding of the intrinsic physico-chemical properties at interfaces is required. To this end, we explore the evolution of interfaces in SSBs directly in atomic scale by machine-learning-driven large-scale molecular dynamics simulations and investigate the structure-property relationship at the interfaces that governs Li-ion transport and stability of SSBs.<br/><br/>In this talk, we will discuss the characteristics of garnet Li₇La₃Zr₂O₁₂ (LLZO) SE/LiCoO₂ (LCO) cathode interfaces as well as the internal interfaces within LLZO. It is observed from our simulations that the propensities for interfacial degradation strongly depend on the surface chemistry of LLZO and LCO. Dopants in LLZO are found to have a segregation effect at the LLZO GBs. Here we will discuss its implication towards secondary phase formation and Li transport kinetics. 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 SSBs, and provide guidance for processing and interface design to achieve desired performance.<br/><br/>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.