Recent Applications of Theoretical Calculations and Artificial Intelligence in Solid-State Electrolyte Research: A Review

Solid-state electrolytes (SSEs), as key materials for all-solid-state batteries (ASSBs), face challenges such as low ionic conductivity and poor interfacial stability. With the rapid advancement of computational science and artificial intelligence (AI) technologies, theoretical calculations and AI methods are emerging as efficient and important virtual tools for predicting and screening high-performance SSEs. To further promote the development of the SSEs, this review outlines recent applications of theoretical calculations and AI in this field. First, the current applications of theoretical calculation methods, such as density functional theory (DFT) and molecular dynamics (MD), in material structure optimization, electronic property analysis, and ionic transport dynamics are introduced, along with an analysis of their limitations. Second, innovative applications of AI methods, including machine learning (ML) and deep learning (DL), in predicting material properties, analyzing structural features, and simulating interfacial behaviors are elaborated. Subsequently, the synergistic application strategies combining high-throughput screening (HTS), theoretical calculations, and AI methods are highlighted, demonstrating the unique advantages of integrating multiple methodologies in material discovery and performance optimization. Finally, the current research progress is summarized, and future development trends are forecasted. The deep integration of theoretical calculations and AI methods is expected to significantly accelerate the development of high-performance SSE materials, thereby driving the industrial application of ASSBs.