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.

The discovery of novel magnetic materials with greater operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are challenging and limited due to the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by introducing a new approach that uses Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 26,706 magnetic materials (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, machine learning models were developed to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (N\'eel) temperature with a coefficient of determination (R2) of 0.86 (0.85) and a mean absolute error (MAE) of 62K (32K). These models identified 62 (19) FM (AFM) candidates with a predicted Curie (N\'eel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models in accelerating the discovery of magnetic materials.

We propose a machine learning approach to predict the shapes of the longitudinal spin fluctuation (LSF) energy landscapes for each local magnetic moment. This approach allows the inclusion of the effects of LSFs in, e.g., the simulation of a magnetic material with ab initio molecular dynamics in an effective way. This type of simulation requires knowledge of the reciprocal interaction between atoms and moments, which, in principle, would entail calculating the energy landscape of each atom at every instant in time. The machine learning approach is based on the kernel ridge regression method and developed using bcc Fe at the Curie temperature and ambient pressure as a test case. We apply the trained machine learning models in a combined atomistic spin dynamics and ab initio molecular dynamics (ASD-AIMD) simulation, where they are used to determine the sizes of the magnetic moments of every atom at each time step. In addition to running an ASD-AIMD simulation with the LSF machine learning approach for bcc Fe at the Curie temperature, we also simulate Fe at temperature and pressure comparable to the conditions at the Earth's inner solid core. The latter simulation serves as a critical test of the generality of the method and demonstrates the importance of the magnetic effects in Fe in the Earth's core despite its extreme temperature and pressure.