Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Recent progress in artificial intelligence (AI) has transformed methodologies across many areas of science. In materials research, AI has enabled more efficient multiscale modeling by linking atomic, mesoscale, and continuum scales with improved accuracy and reduced computational cost. This review examines AI‐based approaches in this context and discusses their relationship to conventional analytical and computational multiscale methods. Developments such as machine learning force fields, graph neural networks, and AI‐accelerated electronic structure prediction are assessed with respect to their capabilities and limitations. To illustrate the current state of the art in this field, available software, computational tools, and benchmarks are discussed. Applications in areas such as phase transitions, defect dynamics, and bulk property prediction are shown, with an emphasis on how AI enhances predictive capabilities. While highlighting the above‐mentioned recent advances, existing challenges and promising directions are also discussed. This review is intended for two audiences: For AI researchers, it demonstrates how physical and chemical constraints influence models’ development to ensure physical consistency, and for physicists, chemists, and materials scientists, it illustrates how AI can improve multiscale methods to solve previously inaccessible problems