| Project/Area Number |
21K14396
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 26010:Metallic material properties-related
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| Research Institution | Japan Advanced Institute of Science and Technology |
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Principal Investigator |
NGUYEN DuongNguyen 北陸先端科学技術大学院大学, 先端科学技術研究科, 助教 (20879978)
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| Project Period (FY) |
2021-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2023: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2022: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | materials discovery / structure prediction / rare-earths magnets / Dempster-Shafer theory / Sm-Fe magnets / evidence-based theory / active learning / SmFe12 / materials exploration / REFe12 magnets |
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| Outline of Research at the Start |
This research aim to model stability mechanisms and monitor the discovery process of RE(Fe1-x-yAxBy)12 magnets using Machine Learning (ML) with RE as rare-earth; A and B as Ga, Co, Mo, Cu, Al, and Ti substituted elements. We build a query-and-learn method comprising Active learning and mechanism-based similarity measurement to learn stability mechanism from the discovery’s feedback. Three results are expected: (1) model stability mechanism by ML, (2) unveil meaningful structure-stability correlations, and (3) monitor the discovery process of REFe12-substituted structures.
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| Outline of Final Research Achievements |
This research aims to build a query-and-learn Machine Learning frameworks to investigate the stability mechanism of REFe magnets with RE as rare-earth element. There are three main goals were achieved. Firstly, this research developed an active learning-based framework to optimize the quantum data querying cost in materials discovery. Quantum calculations for SmFe12 with ThMn12-type magnets with various substitution elements were systematically investigated. Secondly, this research developed an evolutionary algorithm-based framework to optimize multi-objective crystal structure search. The crystal structure-stability relationship of multiple Sm-Fe families was investigated. Lastly, this research developed an evidence-based similarity measure for materials regarding physical property. The method incorporates measurement uncertainty into the similarity measure under the Dempster-Shafer evidence theory
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| Academic Significance and Societal Importance of the Research Achievements |
This work contributes to the research of rare-earth iron-based magnetic materials by (1) finding optimal materials composition of high-performance magnet discovery and (2) introducing Machine Learning frameworks with standardized materials discovery process and results interpretation.
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