用机器学习预测金属有机框架材料性能，随机森林表现最佳。
Machine Learning-Based Prediction of Metal-Organic Framework Materials: A Comparative Analysis of Multiple Models
- 对比五种模型，随机森林在预测精度上最优。
- 随机森林的R²达0.891，均方误差为0.152。
- 适合材料科学领域研究者参考模型选择。
金属-有机框架（MOFs）因其独特的结构特性和多功能性，在多种应用中展现出巨大潜力。本研究系统比较了多种机器学习方法在预测MOF材料性质方面的表现。我们采用随机森林、XGBoost、LightGBM、支持向量机和神经网络五种模型，基于Kaggle平台的数据集进行分析与预测。通过均方根误差（RMSE）、R²、平均绝对误差（MAE）及交叉验证得分等指标评估模型性能。结果表明，随机森林模型表现最优，R²达到0.891，RMSE为0.152，显著优于其他模型。LightGBM则展现出优异的计算效率，训练仅耗时25.7秒且保持高精度。对比分析显示，集成学习方法在MOF性质预测中普遍优于传统单模型。本研究为机器学习在材料科学中的应用提供了重要参考，并构建了未来MOF材料设计与性质预测的可靠框架。
原文摘要 · Abstract (English)
Metal-organic frameworks (MOFs) have emerged as promising materials for various applications due to their unique structural properties and versatile functionalities. This study presents a comprehensive investigation of machine learning approaches for predicting MOF material properties. We employed five different machine learning models: Random Forest, XGBoost, LightGBM, Support Vector Machine, and Neural Network, to analyze and predict MOF characteristics using a dataset from the Kaggle platform. The models were evaluated using multiple performance metrics, including RMSE, R^2, MAE, and cross-validation scores. Results demonstrated that the Random Forest model achieved superior performance with an R^2 value of 0.891 and RMSE of 0.152, significantly outperforming other models. LightGBM showed remarkable computational efficiency, completing training in 25.7 seconds while maintaining high accuracy. Our comparative analysis revealed that ensemble learning methods generally exhibited better performance than traditional single models in MOF property prediction. This research provides valuable insights into the application of machine learning in materials science and establishes a robust framework for future MOF material design and property prediction.
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