用大模型构建6.7万条磁性材料数据库，加速新材料发现
The Northeast Materials Database for Magnetic Materials
- 用大语言模型自动提取实验数据，构建综合性磁性材料库
- 分类准确率达90%，预测居里/奈尔温度误差仅56K/38K
- 适合材料设计与人工智能驱动研发的科研人员
高工作温度和优化性能的磁性材料对先进应用至关重要。当前数据驱动方法受限于缺乏准确、全面且特征丰富的数据库。本研究利用大语言模型（LLMs）构建了一个基于实验数据的综合性磁性材料数据库——东北材料数据库（NEMAD），包含67,573条磁性材料记录（www.nemad.org）。数据库涵盖化学组成、磁相变温度、结构信息及磁性参数。依托NEMAD，我们训练了机器学习模型实现材料分类与相变温度预测。分类模型在区分铁磁（FM）、反铁磁（AFM）和非磁性（NM）材料上达到90%准确率。回归模型对居里（奈尔）温度的决定系数（R²）分别为0.87（0.83），平均绝对误差（MAE）为56K（38K）。基于材料项目（Materials Project）数据，模型识别出25个（13个）预测居里温度高于500K（奈尔温度高于100K）的铁磁（反铁磁）候选材料。该工作展示了结合大模型自动数据提取与机器学习模型在加速磁性材料发现中的可行性。
原文摘要 · Abstract (English)
The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries(www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R2) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.
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