Thermal conductivity is an essential thermodynamic property in chemical engineering application. As a result, estimating the thermal conductivity of organic compounds is of significance in industry production. Alkyl halides are important organic intermediates and raw materials, but little investigations have been performed to estimate their thermal conductivity. In this study, the structures of compounds were optimized in Gaussian 09W and molecular descriptors were extracted by Dragon software. Finally, we developed a 6-descriptor linear quantitative structure-property relationship (QSPR) model to estimate the thermal conductivity of alkyl halides using the genetic function approximation (GFA) method. Validation proved that the developed model had goodness-of-fit, robustness and predictive ability. The r2pred and root-mean-square error (RMSEP) of prediction set for the model were equal to 0.9745 and 0.0035, respectively. Meanwhile, the applicability domain was visualized by means of the Williams plot. This study provides a new model for estimating the thermal conductivity of this important class of chemicals.

中文翻译：

---

用于估计液态烷基卤化物导热系数的稳健模型。

导热系数是化学工程应用中必不可少的热力学性质。结果，估计有机化合物的热导率在工业生产中具有重要意义。卤代烷是重要的有机中间体和原材料，但很少进行研究来估算其导热系数。在这项研究中，化合物的结构在高斯09W中进行了优化，并通过Dragon软件提取了分子描述符。最后，我们建立了一个六描述符线性定量结构-性质关系（QSPR）模型，以使用遗传函数逼近（GFA）方法估算烷基卤的热导率。验证证明所开发的模型具有拟合优度，鲁棒性和预测能力。该模型的预测集的r2pred和均方根误差（RMSEP）分别等于0.9745和0.0035。同时，适用范围通过威廉姆斯图可视化。这项研究为估算这一重要化学物质的热导率提供了一个新模型。