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2015 Fiscal Year Final Research Report

Data-driven approach to condensed-matter physics

Research Project

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Project/Area Number 25610102
Research Category

Grant-in-Aid for Challenging Exploratory Research

Allocation TypeMulti-year Fund
Research Field Mathematical physics/Fundamental condensed matter physics
Research InstitutionThe University of Tokyo

Principal Investigator

Hukushima Koji  東京大学, 総合文化研究科, 准教授 (80282606)

Project Period (FY) 2013-04-01 – 2016-03-31
Keywords物性理論 / 磁性モデル / 磁化曲線 / 機械学習 / モンテカルロ法 / 中性子散乱
Outline of Final Research Achievements

Our main purpose in this project is to develop a data-driven method for materials science. We first propose a systematic method based on the machine learning in which a theoretical model with magnetic interactions is established from an input data of magnetization curve. The proposed method enables us to infer a suitable model among many candidates. Consequently, one may obtain microscopic spin structure which is difficult to see in a conventional experiments and it provides useful information on a following experiment design. It is also found to be efficient to use a Bayesian optimization for quantum spin systems. Meanwhile, dealing with the possibility of handling a big data from large experimental facilities, we develop an inference method of a relaxation-time distribution from neutron-diffraction experiments and offer a new direction including real-data analyses.

Free Research Field

統計物理学

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Published: 2017-05-10  

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