|About this Abstract
||2024 TMS Annual Meeting & Exhibition
||Advanced Soft Magnets and Magnetocaloric Materials: An FMD Symposium in Honor of Victorino Franco
||Machine learning assisted development of magnetocaloric materials
||Hossein Sepehri Amin, E. Dengina, Z. Wang, A. Bolyachkin, X. Tang, T. Ohkubo, K. Hono
|On-Site Speaker (Planned)
||Hossein Sepehri Amin
Magnetic refrigeration based on the magnetocaloric effect (MCE) is known as an efficient technology with potential applications in both ambient and cryogenic temperatures. However, the development of magnetocaloric materials with large and reversible MCE is the main bottleneck for its practical application. We will present how we challenge this issue by alloy design and microstructure engineering [1,2]. We show that the addition of trace amounts of Fe to ErCo2 can lead to the elimination of magneto-structural phase transition while maintaining giant but reversible MCE . The influence of soft magnetic secondary phases on the elimination of thermal hysteresis will be discussed . We will also show how machine learning accelerates development of (Mn,Fe)2(P,Si)-based alloys with large and reversible MCE .
 Xin Tang et al. Nature Comm. 13 (2022) 1817.
 J. Lai et al. Acta Mater. 232 (2022) 117942.
 J. Lai et al. Acta Mater. 220 (2021) 117286.
||Magnetic Materials, Machine Learning, Phase Transformations