Indirect prediction and screening of frustrated magnetic materials using machine learning
Magnetic frustration is commonly quantified by the frustration index, calculated from the Curie-Weiss and Neel temperatures. Because both quantities are not simultaneously available for most materials, direct prediction of the magnetic frustration index remains challenging. Here, we propose a machine learning workflow for indirect prediction of the frustration index and consequent screening of potential frustrated magnetic compounds through independent prediction of Curie-Weiss and Neel temperatures. In our work, composition-based models implemented via the CatBoost algorithm, trained on experimental data were optimized using chemically informed dataset subdivision. The highest predictive accuracy was achieved for the rare earth-oxide model in predicting Neel and for the rare earth model in predicting Curie-Weiss. Ensemble uncertainty was incorporated through a conservative frustration index to improve the reliability of candidate selection. Screening of approximately 67000 Materials Project compounds identified 20 high-confidence frustrated magnetic candidates, including several experimentally known frustrated magnets and previously unexplored oxide systems potential for further investigation.