LIU Dan; LI Yuan; SUN Ruoxuan; QI Xing; SHEN Baogen

doi:10.7498/aps.74.20250431

Abstract

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 them to form compounds with different valence states and coordination environments. Consequently, rare-earth materials typically exhibit excellent magnetic properties and complex magnetic domain structures, making them critical for the development of high-tech industries. The intricate magnetic configurations, different 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 provides researchers with a new method to efficiently analyze vast experimental and computational datasets, thereby accelerating the exploration and development of rare-earth magnetic materials. This work 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. This work also delves into the 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.

Keywords:

Authors and contacts

Corresponding author: LIU Dan, liudan@btbu.edu.cn ; SHEN Baogen, shenbg@iphy.ac.cn

Funds: Project supported by the National Key Research and Development Program of China (Grant No. 2023YFA1610400) and the National Natural Science Foundation of China (Grant Nos. 52088101, 52371169).

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Cited By

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模型	优势	不足
MLR	简单易懂, 计算效率高; 可解释性强, 参数直接反映特征重要性	无法捕捉非线性关系; 对异常值敏感
SVM	擅长处理高维空间中的非线性分类; 小样本数据表现优异	核函数选择依赖经验; 计算成本高, 扩展性差
DT	无需数据标准化; 可视化清晰; 擅长处理分类问题	易过拟合, 需剪枝优化; 对连续特征处理能力有限
RF	抗过拟合能力强, 适合高维数据; 可输出特征重要性, 便于特征选择	计算复杂度较高; 对小样本数据表现不佳
K-means	无需标注数据, 自动发现数据模式; 计算效率高	需要预先指定聚类数
GA	全局搜索能力, 避免局部最优; 并行处理, 适合高维问题	计算成本高; 复杂问题的收敛速度慢; 依赖高质量的训练数据
粒子群优化算法(PSO)	适用于目标函数不连续的场景; 参数少, 易于与实验工具集成	收敛速度慢; 参数敏感性; 局部搜索能力弱
PCA	有效降维, 保留关键信息; 可视化高维数据的潜在结构	无法捕捉非线性关系; 忽略特征间的相关性
ANN	强大的非线性建模能力; 适用于复杂模式识别, 如图像、分子结构	需要大量数据和计算资源; 可解释性差, 存在 “黑箱” 问题
CNN	无需人工设计特征; 擅长高维数据处理; 鲁棒性强	计算资源需求大; 数据依赖性; 解释性差

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Vol. 74, Issue 13 - 2025-07-05

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