Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery

Abstract

Recognizing the critical role of electrolyte chemistry and electrode interfaces in the performance and safety of lithium batteries, along with the urgent need for more sophisticated methods of analysis, this comprehensive review underscores the promise of machine learning (ML) models in this research field. It explores the application of these innovative methods to studying battery interfaces, particularly focusing on lithium metal anodes. Amid the limitations of traditional experimental techniques, the review supports a hybrid approach that couples experimental and simulation methods, enabling granular insights into the formation process and characteristics of battery interfaces at the molecular level and harnessing AI to extract patterns from voluminous data sets. It showcases the utility of such techniques in electrolyte design and battery life prediction and introduces a novel perspective on battery interface mechanisms. The review concludes by asserting the potential of artificial intelligence (AI) or ML models as invaluable tools in the future of battery research and highlights the importance of fostering confidence in these technologies within the scientific community.