Machine learning technique for prediction of magnetocaloric effect in La(Fe, Si/Al)₁₃-based materials

Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La(Fe,Si/Al)₁₃-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree (GBRT) to essentially find the correlation between the Curie temperature (T_C), maximum value of magnetic entropy change ((Δ S_M)_max), and chemical composition, all of which yield high accuracy in the prediction of T_C and (Δ S_M)_max. The performance metric coefficient scores of determination (R²) for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.