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Rare-earth elements possess unique atomic structures characterized by multiple unpaired 4f orbital electrons in inner shells, high atomic magnetic moments, and strong spin-orbit coupling. These attributes endow them with rich electronic energy levels, enabling the formation of compounds with diverse valence states and coordination environments. Consequently, rare-earth materials often exhibit exceptional magnetic properties and complex magnetic domain structures, making them critical for high-tech industrial development. The intricate magnetic configurations, diverse types of magnetic coupling, and direct/indirect magnetic exchange interactions in these materials not only facilitate the development of novel functional devices but also pose significant challenges to fundamental research. With the rapid advancement of data mining techniques, the emergence of big data and artificial intelligence has provided researchers with a new approach to efficiently analyze vast experimental and computational datasets, thereby accelerating the exploration and development of rare-earth magnetic materials. This paper focuses on rare-earth permanent magnetic materials, rare-earth magnetocaloric materials, and rare-earth magnetostrictive materials, detailing the application progress of data mining techniques in property prediction, composition and process optimization, and microstructural analysis. It also delves into current challenges and future trends, aiming to provide a theoretical foundation for deepening the integration of data mining technologies with rare-earth magnetic material research.
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Keywords:
- Data mining /
- Rare earth magnetic material /
- Machine learning /
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