用可解释AI预测磁性材料居里温度，性能达R²=0.85
Explainable AI for Curie Temperature Prediction in Magnetic Materials
- 融合成分与领域特征描述符，用集成学习建模
- 最佳模型R²达0.85（交叉验证），化学分组分析揭示差异
- 通过SHAP识别原子序数和磁矩是关键影响因素
我们利用NEMAD数据库研究机器学习预测磁性材料居里温度的方法。通过引入基于成分和领域知识的描述符，评估多种机器学习模型性能。结果显示，额外树回归器在平衡数据集上表现最优，交叉验证的R²得分高达0.85±0.01。采用k-means聚类分析不同化学类别材料的性能差异。进一步通过SHAP分析，识别出平均原子序数和磁矩等物化属性是影响居里温度的关键因素。该研究结合可解释AI方法，提升了模型预测行为的科学可解释性。
原文摘要 · Abstract (English)
We explore machine learning techniques for predicting Curie temperatures of magnetic materials using the NEMAD database. By augmenting the dataset with composition-based and domain-aware descriptors, we evaluate the performance of several machine learning models. We find that the Extra Trees Regressor delivers the best performance reaching an R^2 score of up to 0.85 $\pm$ 0.01 (cross-validated) for a balanced dataset. We employ the k-means clustering algorithm to gain insights into the performance of chemically distinct material groups. Furthermore, we perform the SHAP analysis to identify key physicochemical drivers of Curie behavior, such as average atomic number and magnetic moment. By employing explainable AI techniques, this analysis offers insights into the model's predictive behavior, thereby advancing scientific interpretability.
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