Query-and-Learn Machine Learning framework to model the stability mechanism of REFe12 magnets

• Project/Area Number: 21K14396
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 26010:Metallic material properties-related
• Research Institution: Japan Advanced Institute of Science and Technology
• 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)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Project Status: Granted (Fiscal Year 2021)
• Budget Amount: ¥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)
• Keywords: materials exploration / REFe12 magnets

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.