Exchange bias is essential for the stability and control of nanoparticles’ magnetic properties for their application as rare-earth-free permanent magnet, magnetic storage, magnetic hyperthermia, and catalysis. Core−shell structures of magnetic bimagnetic particles have garnered increasing interest due to their larger coercive and exchange bias fields, tunable blocking temperatures, and enhanced Neel temperature. However, the design approach of nanoparticles with exchange bias using a computational method has a high computational cost and offers limited efficiency in predicting complex core−shell nanoparticle systems. Machine learning (ML) predictions provide a transformative approach to the design and optimization of materials with desirable exchange bias (EB) properties by offering rapid

Капранова К.А. (науч. рук. Кладько Д.В.) Prediction of exchange bias in magnetic nanoparticles using machine learning // Сборник тезисов докладов конгресса молодых ученых. Электронное издание. – СПб: Университет ИТМО, [2025]. URL: https://kmu.itmo.ru/digests/article/13951