关键词: 启发式方法, 支持向量机, 热导率, 预测, 定量结构-性质关系

Abstract:

To build the quantitative structure-property relationship (QSPR) between the molecular structures and the thermal conductivities of 147 organic compounds and investigate which structural factors influence the thermal conductivity of organic molecules, the topological, constitutional, geometrical, electrostatic, quantum-chemical, and thermodynamic descriptors of the compounds were calculated using the CODESSA software package, where these descriptors were pre-selected by the heuristic method (HM). The dataset of 147 organic compounds was randomly divided into a training set (118), and a test set (29). As a result, a five-descriptor linear model was constructed to describe the relationship between the molecular structures and the thermal conductivities. In addition, a non-linear regression model was built based on the support vector machine (SVM) with the same five descriptors. It was concluded that, although the fitting performance of the SVM model (squared correlation coefficient, R²=0.9240) was slightly worse than that of the HM model (R²=0.9267), the predictive performance of the SVM model (R²=0.9682) was better than that of the HM model (R²=0.9574). As the predictive parameter is more important than the fitting parameter, it can be seen that the SVM model is superior to the HM model. The proposed methods (SVM and HM) can be successfully used to predict the thermal conductivity of organic compounds with pre-selected theoretical descriptors, which can be directly calculated solely from the molecular structure.

Key words: Heuristic method, Support vector machine, Thermal conductivity, Prediction, QSPR