仅用结构信息就能精准预测磁性材料的磁有序与磁矩，加速新材料发现。
Advancing Magnetic Materials Discovery -- A structure-based machine learning approach for magnetic ordering and magnetic moment prediction
- 基于结构信息构建新特征表示，结合光梯度提升树模型进行预测。
- 磁有序分类准确率达82.4%，原子磁矩预测相关系数达0.93。
- 适用于多种材料体系，适合高通量筛选磁性材料的研发人员。
准确预测各类材料系统的磁性行为仍是一大挑战，因其涉及结构与电子因素的复杂耦合，对下一代磁性材料的加速发现与设计至关重要。本文提出一种优化特征表示方法，仅依赖材料结构信息即可显著提升对两类关键磁性性质——磁有序（铁磁与亚铁磁）和原子磁矩——的预测能力。该方法在来自Materials Project的5741种稳定二元与三元化合物数据集上验证，涵盖铁磁与亚铁磁体系，突破了以往仅限于锰基或稀土-过渡金属化合物的局限。通过增强元素向量表示及高级特征工程（含非线性项与稀疏矩阵降维），基于LightGBM的模型实现磁有序分类准确率82.4%，且铁磁与亚铁磁类别的召回率平衡，解决了以往研究的关键缺陷。原子磁矩预测相关系数达0.93，优于传统的洪德矩阵与轨道场矩阵描述符。此外，模型还能准确估计原子形成能，兼顾磁性行为与材料稳定性评估。该通用性强、计算高效的框架为具有特定性能的磁性材料高通量筛选提供了可靠工具。
原文摘要 · Abstract (English)
Accurately predicting magnetic behavior across diverse materials systems remains a longstanding challenge due to the complex interplay of structural and electronic factors and is pivotal for the accelerated discovery and design of next-generation magnetic materials. In this work, a refined descriptor is proposed that significantly improves the prediction of two critical magnetic properties -- magnetic ordering (Ferromagnetic vs. Ferrimagnetic) and magnetic moment per atom -- using only the structural information of materials. Unlike previous models limited to Mn-based or lanthanide-transition metal compounds, the present approach generalizes across a diverse dataset of 5741 stable, binary and ternary, ferromagnetic and ferrimagnetic compounds sourced from the Materials Project. Leveraging an enriched elemental vector representation and advanced feature engineering, including nonlinear terms and reduced matrix sparsity, the LightGBM-based model achieves an accuracy of 82.4% for magnetic ordering classification and balanced recall across FM and FiM classes, addressing a key limitation in prior studies. The model predicts magnetic moment per atom with a correlation coefficient of 0.93, surpassing the Hund's matrix and orbital field matrix descriptors. Additionally, it accurately estimates formation energy per atom, enabling assessment of both magnetic behavior and material stability. This generalized and computationally efficient framework offers a robust tool for high-throughput screening of magnetic materials with tailored properties.
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