2022 Fiscal Year Final Research Report
Magnetic materials exploration using data-driven AI that can explain structure-property mechanisms
| Project/Area Number |
20K05301
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 29010:Applied physical properties-related
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| Research Institution | Japan Advanced Institute of Science and Technology |
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Principal Investigator |
DAM Hieu Chi 北陸先端科学技術大学院大学, 先端科学技術研究科, 教授 (70397230)
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| Co-Investigator(Kenkyū-buntansha) |
水上 卓 北陸先端科学技術大学院大学, 先端科学技術研究科, 助教 (50270955)
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| Project Period (FY) |
2020-04-01 – 2023-03-31
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| Keywords | 磁性材料 / データ駆動型AI / 第一原理計算 |
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| Outline of Final Research Achievements |
We developed an integrated framework of AI and materials science to gain a better understanding of the mechanisms underlying structural properties. Specifically, we systematically explored the candidate space of rare earth-metal (RE)-transition metal (T)-light element (X) crystalline magnetic materials and discovered alternative crystal candidates with high magnetic ability and structural stability. We selected SmFe12, a compound with a ThMn12 structure, as a host lattice due to its excellent magnetic properties. To generate a large number of candidate spaces, we used an automatic generator to substitute elements into each atomic site of the crystalline structure. We established a material search method that integrates first-principles calculations and active learning methods, resulting in the identification of promising candidates for the SmFe12 substitution structures. Our results demonstrate the effectiveness of this approach in discovering new materials with desirable properties.
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| Free Research Field |
マテリアルインフォマティクス
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は構造物性機構の理解を重視したAIと材料科学の首尾一貫的な枠組みを構築し，RE（希土類金属）ーT（遷移金属・構造安定化元素）ーX（軽元素）結晶性磁性材料の候補空間を系統的に探索し，高磁気能で構造安定性をもつ代替結晶候補を発見することと構造物性科学に対して，理解・応用の両面で貢献するした。特に、ThMn12構造を持つ磁石材料SmFe12化合物に対して、網羅的に結晶構造の各原子サイトに元素を置換を行い、第一原理計算と能動学習手法を統合した材料探索法を確立し、SmFe12置換構造の有望な候補を見つけた。この結果、新規磁石材料の開発に貴重な構造、物性、安定性に関する知見を得ることができた。
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