Accelerating the discovery of novel magnetic materials using machine learning–guided adaptive feedback

Journal Article · Mon Nov 14 04:00:00 EST 2022 · Proceedings of the National Academy of Sciences of the United States of America

DOI:https://doi.org/10.1073/pnas.2204485119· OSTI ID:1898202

Xia, Weiyi ^[1]; Sakurai, Masahiro ^[2]; Balasubramanian, Balamurugan ^[3]; Liao, Timothy ^[4]; Wang, Renhai ^[5]; Zhang, Chao ^[6]; Sun, Huaijun ^[7]; Ho, Kai-Ming ^[8]; Chelikowsky, James R. ^[9]; Sellmyer, David J. ^[3]; Wang, Cai-Zhuang ^[1]

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1. Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, Ames Laboratory, U.S. Department of Energy, Iowa State University, Ames, IA 50011
2. Center for Computational Materials, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Chiba 277-8581, Japan
3. Nebraska Center for Materials and Nanoscience, University of Nebraska, Lincoln, NE 68588, Department of Physics and Astronomy, University of Nebraska, Lincoln, NE 68588
4. Center for Computational Materials, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, Department of Physics, The University of Texas at Austin, Austin, TX 78712
5. Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510006, China
6. Department of Physics, Yantai University, Yantai 264005, China
7. Department of Physics, Zhejiang Agriculture and Forestry University, Zhuji 311800, China
8. Department of Physics and Astronomy, Iowa State University, Ames, IA 50011
9. Center for Computational Materials, Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, Department of Physics, The University of Texas at Austin, Austin, TX 78712, McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, TX 78712

Magnetic materials are essential for energy generation and information devices, and they play an important role in advanced technologies and green energy economies. Currently, the most widely used magnets contain rare earth (RE) elements. An outstanding challenge of notable scientific interest is the discovery and synthesis of novel magnetic materials without RE elements that meet the performance and cost goals for advanced electromagnetic devices. Here, we report our discovery and synthesis of an RE-free magnetic compound, Fe ₃ CoB ₂ , through an efficient feedback framework by integrating machine learning (ML), an adaptive genetic algorithm, first-principles calculations, and experimental synthesis. Magnetic measurements show that Fe ₃ CoB ₂ exhibits a high magnetic anisotropy ( K ₁ = 1.2 MJ/m ³ ) and saturation magnetic polarization ( J _s = 1.39 T), which is suitable for RE-free permanent-magnet applications. Our ML-guided approach presents a promising paradigm for efficient materials design and discovery and can also be applied to the search for other functional materials.

• Sponsoring Organization: USDOE
• Grant/Contract Number: AC02-07CH11358
• OSTI ID: 1898202
• Alternate ID(s): OSTI ID: 1915395
• Journal Information: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Issue: 47 Vol. 119; ISSN 0027-8424
• Publisher: Proceedings of the National Academy of Sciences
• Country of Publication: United States
• Language: English