Applying Machine Learning to Design Delicate Amorphous Micro-Nano Materials for Rechargeable Batteries

Abstract

As modern society evolves, the global importance of energy requirements has grown significantly. Thus, exploring new materials for renewable energy storage is urgently needed. Due to its intriguing characteristics, amorphous micro-nanomaterials demonstrate exceptional performance in energy storage. The long experimental periods and high costs associated with traditional methods make it challenging to meet the requirements of materials science. Currently, machine learning (ML) is emerging as a novel research paradigm with the potential to revolutionize the exploration of materials. This review provides an overview of the application of ML in studying amorphous micro-nanomaterials for rechargeable batteries. First, a comprehensive introduction to the fundamentals of amorphous materials and ML is summarized. Then, we highlight a series of representative works to elaborate the relationship between the ML for amorphous nanomaterials. Finally, an insightful perspective on utilization of amorphous materials to establish mature energy storage systems and future directions in ML for amorphous materials are presented. Through this review, we aim to unveil the underlying amorphous structure-function relationship and fuel further research in the design of amorphous materials with outstanding performance across diverse fields.