A review on electrolyte innovation enabled by machine learning for energy storage applications

Progress in solid state energy storage technologies is essential for tackling global energy issues, with electrolytes being crucial for improving the performance, safety, and sustainability of systems such as lithium-ion batteries and next-generation electronics. This review emphasizes the transformative influence of machine learning (ML) in the investigation of electrolyte materials, encompassing liquid, solid-state, polymer, and composite electrolytes. ML enhances material discovery by utilizing computational and experimental data to create predictive models for ionic conductivity, thermal stability, and electrochemical performance, thereby diminishing dependence on resource-intensive trial-and-error approaches. Significant advancements encompass polymer informatics, which associates intricate polymer structures with functional qualities using sophisticated generative models and graph neural networks. Advanced digitization and extensive databases are fundamental to this initiative, facilitating the clarification of structure-property linkages. Furthermore, ML-based optimization of composite electrolytes and ionic liquids tackles design problems, resulting in synergistic enhancements in conductivity, mechanical strength, and stability. These improvements signify a paradigm shift, promoting accelerated innovation cycles and sustainable solutions. This review emphasizes the significance of resilient data ecosystems, interdisciplinary cooperation, and advanced informatics tools in influencing the development of electrolyte materials and energy storage technologies.