| 研究課題/領域番号 |
21K14396
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分26010:金属材料物性関連
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| 研究機関 | 北陸先端科学技術大学院大学 |
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研究代表者 |
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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| 研究期間 (年度) |
2021-04-01 – 2024-03-31
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| 研究課題ステータス |
交付 (2022年度)
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| 配分額 *注記 |
4,420千円 (直接経費: 3,400千円、間接経費: 1,020千円)
2023年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2022年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
2021年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
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| キーワード | materials discovery / rare-earths magnets / evidence-based theory / active learning / SmFe12 / materials exploration / REFe12 magnets |
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| 研究開始時の研究の概要 |
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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| 研究実績の概要 |
The research project has been process with three main achievements:
(1) develop an active learning framework in investigating the structure-stability of SmFe12 family magnets (published in MRS Bulletin Impact). (2) develop an Evidence-based similarity measure for materials science data (published in J. Appl. Phys.) (3) develop a Machine Learning-aided Genetic algorithm to investigate the stability SmFe12 based magnets (published in J. Appl. Phys.)
From data contribution aspect, published articles in (1) and (3) provided systematic quantum calculation datasets for SmFe12 magnets and framework for higher efficiency data calculation. From theoretical contribution aspect, article in (2) investigated a new method bridging between Dempster theory of evidence to investigate materials science data.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
The grant focuses on building a query-and-learn framework to accelerate materials discovery process using interpretable machine learning systems. We clarify the process via the following points:
1- regarding the proposed query-and-learn framework: We have built two calculation infrastructures integrated machine learning and well-known quantum-based calculation methods (VASP and USPEX), which is capable of iteratively calculating new data to investigate a certain materials family
2- regarding accelerating the materials discovery process: lowering calculation resources was performed via our proposed framework
3- regarding interpretable ability: all proposed frameworks introduced methods for researchers to be able to monitor and understand the materials discovery process from multiple viewpoints
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| 今後の研究の推進方策 |
In the future, we continue focus on improving the proposed framework to (1) increasing the efficiency of calculation resource (2) providing higher quality data via extracting more meaningful structure-stability relationship
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