Cheng, MouyangMouyangChengFu, Chu-LiangChu-LiangFuOkabe, RyotaroRyotaroOkabeChotrattanapituk, AbhijatmedhiAbhijatmedhiChotrattanapitukBoonkird, ArtittayaArtittayaBoonkirdTUAN HUNG NGUYENLi, MingdaMingdaLi2026-02-052026-02-05202614761122https://www.scopus.com/record/display.uri?eid=2-s2.0-105026410135&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/735811Materials design is an important component of modern science and technology, yet traditional approaches rely heavily on trial and error and can be inefficient. Computational techniques, enhanced by modern artificial intelligence, have reshaped the landscape of designing new materials. Among these approaches, inverse design has shown great promise in designing materials that meet specific property requirements. In this Review, we present key computational advances in materials design over the past few decades. We follow the evolution of relevant materials design techniques, from high-throughput forward machine learning methods and evolutionary algorithms, to advanced artificial intelligence strategies such as reinforcement learning and deep generative models. We highlight the paradigm shift from conventional screening approaches to inverse generation driven by deep generative models. Finally, we discuss current challenges and future perspectives of materials inverse design. This Review may serve as a brief guide to the approaches, progress and outlook of designing future functional materials with technological relevance.falsereview article10.1038/s41563-025-02403-72-s2.0-105026410135