Machine Learning-Assisted Property Prediction of Solid-State Electrolyte

Machine learning (ML) exhibits substantial potential for predicting the properties of solid-state electrolytes (SSEs). By integrating experimental or/and simulation data within ML frameworks, the discovery and development of advanced SSEs can be accelerated, ultimately facilitating their application in high-end energy storage systems. This review commences with an introduction to the background of SSEs, including their explicit definition, comprehensive classification, intrinsic physical/chemical properties, underlying mechanisms governing their conductivity, challenges, and future developments. An in-depth explanation of the ML methodology is also elucidated. Subsequently, the key factors that influence the performance of SSEs are summarized, including thermal expansion, modulus, diffusivity, ionic conductivity, reaction energy, migration barrier, band gap, and activation energy. Finally, it is offered perspectives on the design prerequisites for upcoming generations of SSEs, focusing on real-time property prediction, multi-property optimization, multiscale modeling, transfer learning, automation and high-throughput experimentation, and synergistic optimization of full battery, all of which are crucial for accelerating the progress in SSEs. This review aims to guide the design and optimization of novel SSE materials for the practical realization of efficient and reliable SSEs in energy storage technologies. This review systematically explores recent advancements in machine learning for solid-state electrolyte properties, focusing on key performance factors such as thermal expansion, modulus, diffusivity, ionic conductivity, reaction energy, migration barrier, band gap, activation energy. It aims to direct future research and guide the selection of optimal solid-state electrolytes for advanced batteries. image