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Ni–Mn–In magnetic shape memory alloys, which can be stimulated by an external magnetic field, exhibit a fast response and have aroused wide attention in the field of electro-mechanical actuators. However, the low working temperature and the inherent brittleness severely limit their application scenarios. Here, an effective strategy is proposed to improve the magnetic-field-induced working temperature and mechanical properties in Ni–Mn–In shape memory alloys. We predict that the Ni16Mn12In4 alloy with Pt doping can solve the problems simultaneously through a comprehensive first-principles study. The calculations show that Pt occupying Ni sites can increase the martensitic temperature ( T M) and Curie temperature ( TC) simultaneously. T M and TC of Ni14Mn12In4Pt2 are predicted to be as high as 440 and 476 K, respectively. This is mainly due to the increased phase stability of the martensite and Pt–Mn bonds having stronger ferromagnetic exchange effects than Ni–Mn bonds after Pt doping. Moreover, according to the increase of B/G and v after Pt doping, it can be concluded that the mechanical properties of the alloy have been improved.

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NiTi-based shape memory alloys (SMAs) are regarded as one of the most promising materials for engineering applications of elastocaloric refrigeration. A critical mission is to efficiently explore the new NiTi-based SMAs with large adiabatic temperature change (ΔTad). We proposed a new material design method that combines highly correlated microscale physical information (volume change, ΔV) into machine learning to predict ΔTad of NiTi-based alloys. First, we tightly coupled machine learning with first-principles calculations to accelerate receiving lattice parameters before and after the phase transformation and predict the ΔV, which shows excellent performance with the coefficient of determination R2 > 0.99. Then, relevant features, including the ΔV, are considered to predict the ΔTad in NiTi-based SMAs. Moreover, due to the small dataset, the principal component analysis and the independent component analysis are added. We evaluate the performance of three machine learning models [Lasso regression, support vector regression, and decision tree regression (DTR)]. Finally, the DTR model exhibits a high accuracy for predicting ΔTad (R2 > 0.9). Introducing the feature of ΔV into the machine learning process can improve the accuracy and efficiency of model design. Further, this work paves the way to accelerate the discovery of new excellent materials for practical applications of elastocaloric refrigeration.