AI 中文总结
本研究提出一种物理引导的计算框架，结合随机森林、伊辛模型蒙特卡洛模拟与自编码器，用于筛选识别有前景的铈基伊辛铁磁体，助力高性能磁性材料开发。
AI 中文摘要
铈（Ce）是丰度最高的镧系元素，在解决高性能磁性材料短缺方面具有巨大潜力，尤其可通过发现适用于间隙磁体的化合物实现这一点。然而，预测具有单轴磁各向异性的铈基铁磁体仍然具有挑战性，因为其磁行为强烈依赖于晶体结构、交换几何和电子相互作用。在此，我们提出一种物理引导的计算框架，用于筛选已知的铈基晶体结构，识别有前景的伊辛铁磁体以供后续合成。随机森林分类器使用7种结构和SOAP描述符，包括晶胞体积、密度、原子位点、空间群、原子密度、Ce SOAP重叠以及过渡金属SOAP重叠，对候选化合物进行优先级排序。随后使用伊辛模型蒙特卡洛模拟分析选定的晶体结构，以表征相行为和临界性质。从模拟相变中提取的临界指数为磁 regime 和各向异性相关效应提供了定量见解。我们还使用基于模拟自旋构型的亲和性特征训练的自编码器，识别相演化和转变行为的潜在特征。该框架整合了结构筛选、统计力学模拟和机器学习，以加速有前景的铈基磁性材料的识别，为实验合成与验证提供候选物。
英文摘要
Cerium (Ce), the most abundant lanthanide, offers significant potential for addressing shortages in high-performance magnetic materials, particularly through the discovery of compounds suitable for gap magnets. However, predicting Ce-based ferromagnets with uniaxial magnetic anisotropy remains challenging because their magnetic behavior depends strongly on crystal structure, exchange geometry, and electronic interactions. Here, we present a physics-guided computational framework to screen known Ce-based crystal structures and identify promising Ising ferromagnets for future synthesis. A Random Forest classifier uses seven structural and SOAP descriptors, including unit-cell volume, density, atomic sites, space group, atomic density, Ce SOAP overlap, and transition-metal SOAP overlap, to prioritize candidate compounds. Selected crystallographic structures are then analyzed using Ising-model Monte Carlo simulations to characterize phase behavior and critical properties. Critical exponents extracted from simulated phase transitions provide quantitative insight into magnetic regimes and anisotropy-related effects. We further employ autoencoders trained on affinity-based features from simulated spin configurations to identify latent signatures of phase evolution and transition behavior. Together, this framework integrates structural screening, statistical-mechanical simulation, and machine learning to accelerate the identification of promising Ce-based magnetic materials and provide candidates for experimental synthesis and validation.
