Explainable AI for Curie Temperature Prediction in Magnetic Materials

Traditional approaches based on quantum mechanical computations or empirical models are often limited in scalability and accuracy. In recent years, machine learning (ML) has emerged as a promising alternative for property prediction across materials science domains [1-9]. Building on this momentum, several recent studies have proposed the use of ML models trained on curated magnetic datasets. In particular, the recent study [10] introduced the NE-MAD database, which aggregates experimentally measured magnetic transition temperatures and compositions. Similarly, the study by [11] utilized two of the largest available datasets of experimental Curie temperatures--comprising over 2,500 materials for training and more than 3,000 entries for validation--to compare machine learning strategies for predicting Curie temperature solely from chemical composition. Our work is inspired by these prior efforts and aims to improve the predictive accuracy and gain insights into model in-terpretability. We develop a pipeline that starts from the NE-MAD dataset, augments it with compositional and elemental features, and evaluates several ML models. A key contribution of our work is the integration of explainable AI (XAI) through SHAP (SHapley Additive exPlanations) analysis, which allows us to quantify how each input feature contributes to the model's prediction. Moreover, we benchmark our models on external datasets from literature to demonstrate generalization.