Machine learning assisted development of Fe2P-type magnetocaloric compounds for cryogenic applications

Fe2P-type compounds exhibit a giant magnetocaloric effect (MCE) and are extensively studied for room temperature applications. The reduction of their transition temperature below 77 K can pave the way for the potential application of these materials for hydrogen liquefaction using cryogenic magnetic refrigeration. Most of the known magnetocaloric materials with a giant MCE below 77 K are rare-earth-based compounds. In order to explore the possibility of developing rare-earth-free compounds with cryogenic MCE, we collected a dataset by conducting data mining on published experimental results on Fe2P-type magnetocaloric compounds and used machine learning for composition optimization aiming at lowering the transition temperature below 77 K. Guided by the predictions of an artificial neural network, we found a promising composition of Mn1.70Fe0.30P0.63Si0.37 with a transition temperature of 97 K at 1 T magnetic field which was lowered to 73 K by the minor substitution of Fe with Co. The developed rareearth-free compounds exhibit a large magnetocaloric performance in isothermal magnetic entropy change ( delta S-M) of 7.5-11.5 J/kgK at the temperatures below 100 K. This study demonstrates that data-driven development of magnetocaloric materials can efficiently boost the optimization of their properties, thus aiding the practical applicability of magnetic refrigeration technology. (C) 2022 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.