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Free keywords:
Artificial neural networks, Decision trees, Flowcharts, Half-metallic materials, Hard magnetic materials, Magnetic alloys, Magnetic and spintronic materials, Magnetic properties, Magnetic separation, Magnetic tunneling, Random forests, Decision trees, Flowcharting, Interfaces (materials), Learning systems, Magnetic materials, Magnetic separation, Flowchart, Half-metallic materials, Hard magnetic material, Machine-learning, Magnetic alloy, Magnetic and spintronic material, Magnetic tunneling, Material Informatics, Random forests, Spintronics materials, Neural networks
Abstract:
This report summarizes the recent development of magnetic materials search using artificial intelligence (AI) and machine learning (ML). The report briefly introduces ML and AI approaches to materials discovery, and the authors offer a flowchart to aid the selection of relevant approaches for their material search. The report 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. Author
