Development of machine learning methods for materials informatics

2017 Fiscal Year Final Research Report

• Project Area: Exploration of nanostructure-property relationships for materials innovation
• Project/Area Number: 16H00736
• Research Category: Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
• Allocation Type: Single-year Grants
• Review Section: Science and Engineering
• Research Institution: The University of Tokyo
• Principal Investigator: Tsuda Koji 東京大学, 大学院新領域創成科学研究科, 教授 (90357517)
• Co-Investigator(Kenkyū-buntansha): 志賀 元紀 岐阜大学, 工学部, 准教授 (20437263) ; 鹿島 久嗣 京都大学, 情報学研究科, 教授 (80545583)
• Co-Investigator(Renkei-kenkyūsha): TAKEUCHI Ichiro 名古屋工業大学, 工学(系)研究科, 教授 (40335146)
• Project Period (FY): 2016-04-01 – 2018-03-31
• Keywords: マテリアルズインフォマティクス

Outline of Final Research Achievements

This research started from 2016 to boost the development of informatics methods in materials development. As a result of collaborations with other groups, we obtained the following achievements. (1) Statistical machine learning methods for spectrum analysis to discovering nanostructures. (2) Automatic design of materials using Monte Carlo tree search. (3) Discovery of stable compounds with recommendation algorithms.

Free Research Field

機械学習、バイオインフォマティクス