From Atoms to Algorithms: Machine Learning Approaches to Cathode Material Innovation in Zinc-Ion Batteries

Abstract

Zinc-ion batteries (ZIBs) have emerged as strong contenders for future energy storage technologies owing to their inherent safety, low cost and eco-friendly nature. Despite this, achieving high-performance cathode materials continues to be a significant hurdle in their advancement. This review explores the application of machine learning (ML) to accelerate the discovery and optimization of cathode materials for zinc-ion batteries (ZIBs). Beyond material selection, we also emphasize how ML methodologies extend to predicting and optimizing critical parameters such as synthesis methods and electrode-electrolyte interactions, factors that significantly influence the electrochemical performance of ZIBs. By leveraging diverse ML models and algorithms, researchers can predict key material properties, evaluate structure-function relationships, and assess the viability of candidate materials for cathode applications. The review further discusses the integration of ML in informatics-driven design and synthesis route optimization while addressing challenges including limited data availability, model interpretability, and the need for experimental validation. Finally, we highlight future directions such as the development of standardized datasets, incorporation of explainable AI, and the convergence of experimental, computational workflows to unlock the full potential of ML in advancing next-generation ZIB technologies.