用特征工程和不平衡处理提升杂化卤化物维度预测准确率
Enhancing Dimensionality Prediction in Hybrid Metal Halides via Feature Engineering and Class-Imbalance Mitigation
- 设计化学感知的交互特征，构建多阶段预测流程
- 在494个结构基础上扩增至1336个，提升小类预测效果
- 对0D/1D等稀有维度表现优异，适合材料研发人员使用
我们提出一种机器学习框架，用于预测杂化金属卤化物（HMHs）的结构维度（包括有机-无机钙钛矿），结合化学启发的特征工程与先进的类别不平衡处理技术。数据集包含494个HMH结构，维度类别（0D、1D、2D、3D）分布严重不均，给建模带来挑战。通过合成少数类过采样技术（SMOTE）将数据量扩充至1336个，缓解类别不平衡问题。我们开发了基于相互作用的描述符，并集成到多阶段工作流中，包含特征选择、模型堆叠与性能优化，显著提升了低频类别（如0D、1D）的F1分数，在所有维度上均实现稳健的交叉验证表现。
原文摘要 · Abstract (English)
We present a machine learning framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. This dataset was later augmented to 1336 via the Synthetic Minority Oversampling Technique (SMOTE) to mitigate the effects of the class imbalance. We developed interaction-based descriptors and integrated them into a multi-stage workflow that combines feature selection, model stacking, and performance optimization to improve dimensionality prediction accuracy. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities.
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