Are the sublimation thermodynamics of organic molecules predictable?
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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 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 dataset 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.
McDonagh , J L , Palmer , D S , van Mourik , T & Mitchell , J B O 2016 , ' Are the sublimation thermodynamics of organic molecules predictable? ' Journal of Chemical Information and Modeling , vol. 56 , no. 11 , pp. 2162-2179 . https://doi.org/10.1021/acs.jcim.6b00033
Journal of Chemical Information and Modeling
Copyright © 2016 American Chemical Society. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at: https://doi.org/10.1021/acs.jcim.6b00033
DescriptionJMcD and JBOM would like to thank SULSA for funding. DSP thanks the University of Strathclyde for support through its Strategic Appointment and Investment Scheme.
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