用机器学习纠正材料数据库中的磁性误判，提升分类准确率。
Learning Magnetic Order Classification from Large-Scale Materials Databases
- 基于成分、结构和电子描述符训练分类器，识别磁性基态
- 传播矢量分类准确率超92%，发现材料库存在铁磁偏差
- 适合大规模磁性材料筛选与数据库质量优化
可靠识别磁性基态仍是高通量材料数据库中的重大挑战，密度泛函理论（DFT）工作流常收敛于铁磁（FM）解。本文通过在实验验证的MAGNDATA磁性材料数据集上训练机器学习分类器，利用来自Materials Project数据库的少量简单组成、结构和电子描述符，部分解决该问题。其传播矢量分类器准确率超过92%，优于近期研究，可有效区分零与非零传播矢量结构，并揭示Materials Project数据库对超过7,843种材料存在系统性铁磁偏差。同时，基于Materials Project标签训练的LightGBM与XGBoost模型准确率达84-86%（宏F1平均分63-66%），适用于大规模磁性类别筛查，若经MAGNDATA训练分类器修正则更可靠。结果表明，机器学习可作为校正与探索工具，提升数据库可信度，加速各类材料的发现进程。
原文摘要 · Abstract (English)
The reliable identification of magnetic ground states remains a major challenge in high-throughput materials databases, where density functional theory (DFT) workflows often converge to ferromagnetic (FM) solutions. Here, we partially address this challenge by developing machine learning classifiers trained on experimentally validated MAGNDATA magnetic materials leveraging a limited number of simple compositional, structural, and electronic descriptors sourced from the Materials Project database. Our propagation vector classifiers achieve accuracies above 92%, outperforming recent studies in reliably distinguishing zero from nonzero propagation vector structures, and exposing a systematic ferromagnetic bias inherent to the Materials Project database for more than 7,843 materials. In parallel, LightGBM and XGBoost models trained directly on the Materials Project labels achieve accuracies of 84-86% (with macro F1 average scores of 63-66%), which proves useful for large-scale screening for magnetic classes, if refined by MAGNDATA-trained classifiers. These results underscore the role of machine learning techniques as corrective and exploratory tools, enabling more trustworthy databases and accelerating progress toward the identification of materials with various properties.
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