From atoms to algorithms: a review of machine learning approaches to cathode material innovation in zinc-ion batteries

This review explores how ML accelerates data-driven design and synthesis of Zn-ion batteries, addressing data gaps, interpretability, and validation while emphasizing standardized datasets, explainable AI, and experiment–theory integration.