| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2013: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| 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.
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