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arXiv · 2610.15683

Indirect prediction and screening of frustrated magnetic materials using machine learning

Regina Burganova·Irina Piyanzina·Ruslan Akhmerov·Alexander Sigov·Maxim Fedorov
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Abstract

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.

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Regina Burganova, Irina Piyanzina, Ruslan Akhmerov, Alexander Sigov, Maxim Fedorov. 2026-10-11. Indirect prediction and screening of frustrated magnetic materials using machine learning. https://arxiv.org/abs/2610.15683

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