[2509.05909] Learning Magnetic Order Classification from Large-Scale Materials Databases

Condensed Matter > Materials Science arXiv:2509.05909 (cond-mat) [Submitted on 7 Sep 2025 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Learning Magnetic Order Classification from Large-Scale Materials Databases Authors:Ahmed E. Fahmy View a PDF of the paper titled Learning Magnetic Order Classification from Large-Scale Materials Databases, by Ahmed E. Fahmy View PDF Abstract:The reliable identification of magnetic ground states remains a major challenge in high-throughput materials databases, where density functional theory (DFT) workflows often converge to ferromagnetic (FM) solutions. Here, we partially address this challenge by developing machine learning classifiers trained on experimentally validated MAGNDATA magnetic materials leveraging a limited number of simple compositional, structural, and electronic descriptors sourced from the Materials Project database. Our propagation vector classifiers achieve accuracies above 92%, outperforming recent studies in reliably distinguishing zero from nonzero propagation vector structures, and exposing a systematic ferromagnetic bias inherent to the Materials Project database for more than 7,843 materials. In parallel, LightGBM and XGBoost models trained directly on the Materials Project labels achieve accuracies of 84-86% (with macro F1 average scores of 63-66%), which proves useful for large-scale screening for magnetic classes, if refined by MAGNDATA-trained classifiers. These results underscore the role of machin...