Regensburg 2025 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 3: Data-driven Materials Science: Big Data and Worksflows
MM 3.7: Talk
Monday, March 17, 2025, 12:00–12:15, H10
Data-driven design of mechanically hard soft magnetic high-entropy alloys — •Mian Dai1, Yixuan Zhang1, Xiaoqing Li2, Stephan Schönecker2, Liuliu Han3, Ruiwen Xie1, Chen Shen1, and Hongbin Zhang1 — 1Institute of Materials Science, Technical University of Darmstadt, Darmstadt, Germnay — 2Department of Materials Science and Engineering, KTH - Royal Institute of Technology, Stockholm, Sweden — 3Max Planck Institute for Sustainable Materials, Düsseldorf, Germany
The rational design of mechanically hard soft magnets, combining high hardness with magnetically soft properties, represents a critical frontier in materials science. Here, we introduce a comprehensive data-driven framework to navigate the vast compositional space of high-entropy alloys (HEAs) and identify candidates optimized for these dual functionalities. Utilizing a curated dataset of 1,842,628 density functional theory calculations, encompassing 45,886 quaternary and 414,771 quinary equimolar HEAs derived from 42 elements, we employ ensemble learning to synergistically integrate multiple predictive models. This methodology captures the relationships between composition, crystal structure, mechanical performance, and magnetic behavior, enabling the identification of alloys with a unique combination of high hardness and magnetic softness. Our framework establishes a robust pathway for the accelerated discovery of next-generation hard-soft magnetic materials, underscoring the transformative potential of data-driven strategies in materials design.
Keywords: High-entropy alloys; High-throughput calculations; Mechanically hard soft magnets; Density functional theory; Machine learning