Materials informatics (MI) has gained attention as a data-driven approach to accelerating material optimization in several fields. By leveraging computational methods, recent advancements in artificial intelligence, and large datasets, MI is poised to significantly accelerate the development of various materials. This approach drives innovative solutions, offers new insights into composition-property relationships, and potentially redefines the standards for material testing and selection. Consequently, MI has become a powerful alternative to traditional experimentation, offering more systematic and efficient pathways for materials innovation. Despite its success in other domains, only a handful of studies have used MI in dental materials research, which has long relied on iterative, labor-intensive empirical testing to make incremental improvements. This narrative review summarizes the fundamental principles of MI while showcasing its emerging role in dental materials research. We also discuss advantages of MI compared with traditional pipelines; current limitations, such as data scarcity and the challenges of translating in vitro findings to clinical outcomes; and future perspectives.

AI Research Unit Graduate School of Dentistry The University of Osaka Suita Osaka Japan

Department of Conservative Dentistry and Periodontology LMU University Hospital LMU Munich Munich Germany

Department of Dental Biomaterials Graduate School of Dentistry The University of Osaka Suita Osaka Japan

Institute of Dental Medicine 1st Faculty of Medicine of the Charles University and General University Hospital Prague Prague Czech Republic

Joint Research Laboratory of Advanced Functional Materials Science Graduate School of Dentistry The University of Osaka Suita Osaka Japan

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