2023 Fiscal Year Annual Research Report
Query-and-Learn Machine Learning framework to model the stability mechanism of REFe12 magnets
| Project/Area Number |
21K14396
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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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| Keywords | materials discovery / structure prediction / rare-earths magnets / Dempster-Shafer theory / Sm-Fe magnets |
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| Outline of Annual Research Achievements |
Toward the goal of building query-and-learn Machine Learning framework to investigate REFe magnets, there are three objectives were achieved:
(1) develop an active learning-based framework to optimize the quantum calculation cost of querying data in materials discovery. Quantum calculations for SmFe12 with ThMn12-type magnets with various substitution elements were systematically investigated
(2) develop an evolutionary algorithm-based framework to optimize multi-objective crystal structure search. The crystal structure-stability relationship of multiple Sm-Fe families was investigated
(3) develop 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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Research Products
(6 results)
