In the field of materials R&D—which has relied on trial and error for years—“materials informatics” (MI), a scientific approach that utilizes artificial intelligence (AI) technologies to predict structures and production methods of materials with desired performance from enormous research data, is now sweeping the world. Results exceeding previous expectations are now being reported one after the other, and concrete new material development is being made through MI rapidly. What is the key to data-driven materials R&D that differs in essence from conventional simulation technologies?
In addition to construction of computational data infrastructure under intense competition, AI technologies and high-quality materials databases for analyzing big data are essential. The precision of machine learning using massive quantities of research data hangs on the quality and quantity of data.
At this year’s NIMS Academic Symposium, NIMS Award will be presented to researchers who have achieved remarkable results in data-driven materials design and database development on a global scale. Furthermore, the lectures presented by researchers at the forefront of the field are sure to provide a bird’s eye view of the most recent advances in and future outlook for materials research utilizing AI and data.
Speakers have been requested to make their presentations understandable to researchers, engineers, and students who are not specialists in the relevant fields. Simultaneous Japanese-English interpretation will be provided for all lectures.
We welcome participation by researchers, professionals, and students from a broad range of fields.