Topic: AI-Driven Discovery and Design of Energy Materials

Artificial intelligence and machine learning are reshaping the research paradigm of electrochemical energy materials, enabling rational design ranging from molecular-level electrolyte formulation to device-level performance optimization, and drastically cutting the lengthy trial-and-error cycle of conventional R&D. Key bottlenecks hindering the commercial rollout of high-energy storage technologies include solvation structure tuning, stable electrode-electrolyte interphases, thermal safety risks, and compatibility issues in solid-state battery systems.

This Special Issue, AI-Driven Discovery and Design of Energy Materials, focuses specifically on electrochemical energy storage and conversion systems covering batteries, supercapacitors, and electrocatalysis. It prioritizes contributions centered on AI-accelerated material screening, solvation chemistry modulation, interphase engineering, solid-state electrolyte development, in-situ characterization, and battery performance optimization. The collection unites cutting-edge computational and experimental breakthroughs, building a multidisciplinary platform to propel the advancement of next-generation high-performance electrochemical energy storage and conversion devices.

AI-driven materials design; Solvation chemistry; Interphase engineering; Electrochemical energy storage; Solid-state electrolytes; Thermal safety; In-situ characterization; Battery performance optimization.