Are the Sublimation Thermodynamics of Organic Molecules Predictable?

We
compare a range of computational methods for the prediction
of sublimation thermodynamics (enthalpy, entropy, and free energy
of sublimation). These include a model from theoretical chemistry
that utilizes crystal lattice energy minimization (with the DMACRYS
program) and quantitative structure property relationship (QSPR) models
generated by both machine learning (random forest and support vector
machines) and regression (partial least squares) methods. Using these
methods we investigate the predictability of the enthalpy, entropy
and free energy of sublimation, with consideration of whether such
a method may be able to improve solubility prediction schemes. Previous
work has suggested that the major source of error in solubility prediction
schemes involving a thermodynamic cycle via the solid state is in
the modeling of the free energy change away from the solid state.
Yet contrary to this conclusion other work has found that the inclusion
of terms such as the enthalpy of sublimation in QSPR methods does
not improve the predictions of solubility. We suggest the use of theoretical
chemistry terms, detailed explicitly in the Methods section, as descriptors
for the prediction of the enthalpy and free energy of sublimation.
A data set of 158 molecules with experimental sublimation thermodynamics
values and some CSD refcodes has been collected from the literature
and is provided with their original source references.

本研究对比了多种用于预测升华热力学（升华焓、升华熵及升华自由能）的计算方法。这些方法涵盖了基于理论化学、通过晶体晶格能量最小化（借助DMACRYS程序实现）构建的模型，以及由机器学习（随机森林与支持向量机）和回归（偏最小二乘）方法生成的定量结构-性质关系（Quantitative Structure Property Relationship, QSPR）模型。本研究利用上述方法探究了升华焓、升华熵及升华自由能的可预测性，并考量此类方法是否能够优化溶解度预测流程。既往研究指出，在基于固态热力学循环的溶解度预测流程中，误差的主要来源为偏离固态时的自由能变化建模环节。但另有研究得出了与之相悖的结论：在QSPR模型中引入升华焓等参数，并未对溶解度预测结果起到优化作用。本研究建议采用方法章节中详细说明的理论化学参数作为描述符，用于升华焓与升华自由能的预测。本研究从已发表文献中整理得到一个包含158种分子的数据集，这些分子带有实验测得的升华热力学数值及部分剑桥晶体结构数据库（Cambridge Structural Database, CSD）参考码，并附带其原始文献来源标注。