This Collection invites contributions that explore how artificial intelligence accelerates materials science—from atomic-level design to device integration—through data-driven approaches, predictive modeling, autonomous experimentation, and enhanced characterization techniques. Emphasis is placed on broadly applicable workflows, rigorous validation, benchmarking, and the release of reusable datasets, protocols, or software.