Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Abstract 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…

• NN
  National Natural Science Foundation of China

  Awards: 52072323, 52122211, 52122408, 22008098, 52374395