Exploring Secondary Phase Formation at The Solid-Electrolyte/Cathode Interface using Machine-Learning Interatomic Potential

All-solid-state Li-ion batteries are attractive next-generation energy-storage devices, offering improved safety, energy density, and durability compared to conventional Li-ion batteries. A critical challenge in these batteries is the occurrence of side reactions at the solid-electrolyte/cathode interface, particularly at elevated temperatures during co-sintering. These reactions have the potential to result in the undesired formation of secondary phases that impede the transport of lithium ions. The dynamic formation of these secondary phases at the atomic scale and the conditions governing their emergence remain unclear.<br/>In this work, we explore the nucleation and evolution of secondary phases, such as La-Co-O, at the interface between the Li₇La₃Zr₂O₁₂ (LLZO) solid electrolyte and the LiCoO₂ (LCO) cathode. Our investigation comprises three main components that employ atomistic simulations driven by a machine-learning potential (MLP) for accelerated and comprehensive analysis. Firstly, we utilize a crystal structure prediction algorithm in conjunction with an MLP to accelerate the identification of potential secondary phases that may form at the interface. Secondly, we conduct MLP-driven metadynamics simulations to investigate the relationship between local structural features and the energy landscape associated with the nucleation of secondary phases. This exploration helps us understand the conditions leading to the formation of bulk-like secondary phases. Finally, we perform large-scale MLP molecular dynamics simulations to directly observe the formation of secondary phases in a model interface structure.<br/>Through this multi-level investigation, we offer a holistic understanding of the formation of secondary phases at the LLZO/LCO interface. This insight is critical for understanding battery degradation resulting from interface reactions.<br/><br/>This work was sponsored by the Office of Energy Efficiency and Renewable Energy, Vehicle Technologies Office and was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. A portion of this research was performed using computational resources sponsored by the Department of Energy's Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory.