Prediction of Vaporization Enthalpy of Pure Compounds using a Group Contribution-Based Method

In this work, the artificial neural network–group contribution (ANN-GC) method is applied to estimate the vaporization enthalpy of pure chemical compounds at their normal boiling point. A group of 4907 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient (R2) of 0.993, root mean square error of 1.1 kJ/mol, and average absolute deviation lower than 1.5% for the estimated properties from existing experimental values.本研究采用人工神经网络-基团贡献（Artificial Neural Network-Group Contribution, ANN-GC）方法，对纯化合物在正常沸点下的汽化焓进行估算。本研究选取4907种涵盖不同化学族类的纯化合物开展研究，以构建一套兼具全面性与预测性能的模型。所得结果显示：相较于现有实验值，该模型估算得到的物性数据的决定系数（R²）为0.993，均方根误差为1.1 kJ/mol，平均绝对偏差低于1.5%。