Machine Learning Assisted Development of Fe2P-type Magnetocaloric Compounds for Cryogenic Applications
Machine Learning Assisted Development of Fe2P-type Magnetocaloric Compounds for Cryogenic Applications
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DOI：
10.1016/j.actamat.2022.117942
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发表时间：
2022-04
期刊：
影响因子：
9.4
通讯作者：
J. Lai;A. Bolyachkin;N. Terada;S. Dieb;Xin Tang;T. Ohkubo;H. Sepehri-Amin;K. Hono
中科院分区：
文献类型：
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作者：
J. Lai;A. Bolyachkin;N. Terada;S. Dieb;Xin Tang;T. Ohkubo;H. Sepehri-Amin;K. Hono
Fe 2 P型化合物表现出巨磁热效应（MCE），并且被广泛研究用于室温应用。将它们的转变温度降低到77 K以下可以为这些材料用于使用低温磁制冷的氢液化的潜在应用铺平道路。大多数已知的具有低于77 K的巨大MCE的磁热材料是稀土基化合物。为了探索利用低温MCE开发无稀土化合物的可能性，我们通过对Fe 2 P型磁热化合物的已发表实验结果进行数据挖掘来收集数据集，并使用机器学习进行成分优化，旨在将转变温度降低到77 K以下。在人工神经网络预测的指导下，我们发现了一种很有前途的化合物Mn1.70Fe0.30P0.63Si0.37，其在1 T磁场下的转变温度为97 K，通过用Co少量取代Fe，将其降低到73 K。所开发的无稀土化合物在7.5- 1.5 μ m的等温磁熵变（MASM）下表现出大的磁热性能。在100 K以下温度下为11.5 J/kgK。这项研究表明，磁热材料的数据驱动开发可以有效地促进其性能的优化，从而有助于磁制冷技术的实用性。
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 Mn1.70Fe0.30P0.63Si0.37with 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 rare-earth-free compounds exhibit a large magnetocaloric performance in isothermal magnetic entropy change (∆SM) 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.