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Recent advancements in artificial intelligence (AI), particularly in algorithms and computing power, have led to the widespread adoption of AI techniques in various scientific and engineering disciplines. Among these, materials science has seen a significant transformation due to the availability of vast datasets, through which AI techniques, such as machine learning (ML) and deep learning (DL), can solve complex problems. One area where AI is proving to be highly impactful is in the design of high-performance Li-ion batteries (LIBs). The ability to accelerate the discovery of new materials with optimized structures using AI can potentially revolutionize the development of LIBs, which are important for energy storage and electric vehicle technologies. However, while there is growing interest in using AI to design LIBs, the application of AI to discover new electrolytic systems for LIBs needs more investigation. The gap in existing research lies in the lack of a comprehensive framework that integrates AI-driven techniques with the specific requirements for electrolyte development in LIBs. This research aims to fill this gap by reviewing the application of AI for discovering and designing new electrolytic systems for LIBs. In this study, we outlined the fundamental processes involved in applying AI to this domain, including data processing, feature engineering, model training, testing, and validation. We also discussed the quantitative evaluation of structure–property relationships in electrolytic systems, which is guided by AI methods. This work presents a novel approach to use AI for the accelerated discovery of LIB electrolytes, which has the potential to significantly enhance the performance and efficiency of next-generation battery technologies.
Alzamer et al. (Thu,) studied this question.