Accurate prediction of magnetocaloric effect in NiMn-based Heusler alloys by prioritizing phase transitions through explainable machine learning

With the rapid development of artificial intelligence, magnetocaloric materials as well as other materials are being developed with increased efficiency and enhanced performance. However, most studies do not take phase transitions into account, and as a result, the predictions are usually not accurate enough. In this context, we have established an explicable relationship between alloy compositions and phase transition by feature imputation. A facile machine learning is proposed to screen candidate NiMn-based Heusler alloys with desired magnetic entropy change and magnetic transition temperature with a high accuracy R²≈0.98. As expected, the measured properties of prepared NiMn-based alloys, including phase transition type, magnetic entropy changes and transition temperature, are all in good agreement with the ML predictions. As well as being the first to demonstrate an explicable relationship between alloy compositions, phase transitions and magnetocaloric properties, our proposed ML model is highly predictive and interpretable, which can provide a strong theoretical foundation for identifying high-performance magnetocaloric materials in the future.