Predicting the Curie temperature of Sm-Co-based alloys via data-driven strategy

Calculating the Curie temperature of rare-earth permanent magnetic materials has remained a big theoretical challenge. In this study, based on a home-built Sm-Co-based alloys database, a data-driven machine learning approach was developed to predict the Curie temperature of Sm-Co-based alloys. High-throughput predictions of Curie temperature were achieved using a genetic program based symbolic regression model. A classification model based on logistic regression was established to quantify the effect of doping on the Curie temperature of Sm-Co-based alloys. The key physical descriptor affecting Curie temperature was extracted from the established machine learning models, and the Curie temperature sensitivity coefficient was defined. It was discovered that the doping elements with large electrical conductivity and similar heat of fusion to that of Sm are likely to increase the Curie temperature of Sm-Co-based alloys. The model predictions were verified quantitatively by the experimental results of a series of prepared Sm-Co-based samples. This work provides a high-efficiency method for developing Sm-Co-based permanent magnets with high Curie temperatures. A universal rule for predicting the Curie temperature of Sm-Co-based alloys was proposed based on the data-driven method. It was discovered that doping elements with a large electrical conductivity and similar heat of fusion with Sm are candidates to increase the Curie temperature of Sm-Co-based alloys. A Curie-temperature sensitivity factor was defined to reveal the integrated effect of key factors on the Curie temperature of Sm-Co-based alloys. This study provides a new approach for developing Sm-Co-based permanent magnetic materials with high Curie temperatures. [Display omitted]