Materials Informatics for the Development and Discovery of Future Magnetic Materials

• Published in: IEEE magnetics letters Vol. 14; pp. 1 - 5
• Main Authors: Okabe, Ryotaro, Li, Mingda, Iwasaki, Yuma, Regnault, Nicolas, Felser, Claudia, Shirai, Masafumi, Kovacs, Alexander, Schrefl, Thomas, Hirohata, Atsufumi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Artificial neural networks; Condensed Matter; Decision trees; Engineering; Flow charts; Flowcharts; half-metallic materials; hard magnetic materials; Heusler alloys; Machine learning; magnetic alloys; magnetic and spintronic materials; Magnetic materials; Magnetic properties; Magnetic separation; Magnetic tunneling; Magnetism in solids; Materials information; Metallic materials; Metals; Permanent magnets; Physics; Random forests; Solids; Spintronics; Hard magnetic materials; Half-metallic materials; Magnetic alloys; Magnetic and spintronic materials
• ISSN: 1949-307X; 1949-3088
• DOI: 10.1109/LMAG.2023.3320888

This letter summarizes the recent development of magnetic materials search using artificial intelligence (AI) and machine learning (ML) and briefly introduces ML and AI approaches to materials discovery. The authors offer a flowchart to aid the selection of relevant approaches for their material search. This letter also covers the authors' recent research activities in magnetism and quantum materials, including topological materials, Heusler alloys, interfaces, and permanent magnets. This overview is based on a recent symposium at IEEE Intermag 2023.