Role of AI in experimental materials science

Over the past five years, artificial intelligence (AI) has grown significantly in different aspects of our daily lives, including health, transportation, and the digital world, all by leveraging data. Inspired by these success stories, materials researchers have started to adopt AI in experimental materials science to accelerate materials discovery and development by 10–100× through improving the efficiency of hypothesis generation, testing, and data analysis in a closed-loop fashion. This issue of MRS Bulletin presents a collection of papers discussing the recent advancements of AI in different aspects of experimental materials science and provides a framework for the next generation of autonomous experimentation strategies. In this article, we review the role of AI in experimental materials science and summarize the key aspects and challenges of autonomous experimentation discussed in each contributed article. We pose four questions at the interface of AI and experimental materials science, and present immediate calls for action for researchers working in this emerging field to move beyond optimization toward autonomous discovery. We hope this issue can accelerate convergence as well as flexibility and reconfiguration of hardware and software modules of autonomous robotic experimentation techniques to enable true digitalization of materials synthesis.