AI RESEARCH
Learning Magnetic Order Classification from Large-Scale Materials Databases
arXiv CS.LG
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ArXi:2509.05909v2 Announce Type: replace-cross 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.