Topic: AI-Driven Design and Discovery of Energy Materials: Methods, Applications, and Frontiers

The advancement of sustainable energy systems demands accelerated discovery and optimization of high-performance energy materials. Traditional trial-and-error experimental approaches cannot efficiently explore the enormous compositional and structural design space, while artificial intelligence has emerged as a transformative solution bridging computation, simulation and laboratory experiments. By integrating machine learning, generative models, high-throughput screening and autonomous experimental workflows, AI enables fast inverse design, performance prediction and mechanism decoding of functional energy materials.

Aligned with the core mission of Journal of Materials Informatics to advocate open data sharing, open-source tool development and fully reproducible materials research, this Special Topic titled AI-Driven Design and Discovery of Energy Materials: Methods, Applications, and Frontiers gathers interdisciplinary advances across materials informatics, computational modeling and energy device engineering. We welcome original research articles, comprehensive reviews, perspectives and commentaries covering explainable AI, digital material twins, autonomous labs, multimodal data analysis, as well as their practical applications in batteries, electrocatalysis, photovoltaics, thermoelectrics and carbon capture. This collection aims to foster cross-field collaboration and promote transparent, reproducible AI workflows to accelerate the industrial translation of next-generation sustainable energy materials.

Artificial Intelligence (AI), Materials Informatics, Machine Learning, Energy Materials, Inverse Materials Design, High-Throughput Screening, Autonomous Laboratories, Digital Twins, Explainable AI, Sustainable Energy Technologies.