Explainable AI for Curie Temperature Prediction in Magnetic Materials
This work addresses the need for accurate and interpretable predictions of magnetic properties in materials science, though it is incremental as it applies existing methods to a specific domain.
The researchers tackled the problem of predicting Curie temperatures in magnetic materials using machine learning, achieving an R^2 score of up to 0.85 ± 0.01 with an Extra Trees Regressor on a balanced dataset. They also used SHAP analysis to identify key physicochemical drivers like average atomic number and magnetic moment for interpretability.
We explore machine learning techniques for predicting Curie temperatures of magnetic materials using the NEMAD database. By augmenting the dataset with composition-based and domain-aware descriptors, we evaluate the performance of several machine learning models. We find that the Extra Trees Regressor delivers the best performance reaching an R^2 score of up to 0.85 $\pm$ 0.01 (cross-validated) for a balanced dataset. We employ the k-means clustering algorithm to gain insights into the performance of chemically distinct material groups. Furthermore, we perform the SHAP analysis to identify key physicochemical drivers of Curie behavior, such as average atomic number and magnetic moment. By employing explainable AI techniques, this analysis offers insights into the model's predictive behavior, thereby advancing scientific interpretability.