Are the Sublimation Thermodynamics of Organic Molecules Predictable?

Abstract

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 2 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.

State-of-the-Art Calculations of Sublimation Enthalpies for Selected Molecular Crystals and Their Computational Uncertainty

“…[4][5][6] Moreover, empiric and semi-empiric estimative methods for predictions, based for example on the quantitative structure property relationship (QSPR) models, are not generally reliable or purely predictive, although effort is still exerted in this area. 7,8 On the other hand, the rapid increase of number of known chemical species and corresponding resolved crystal structures does not correlate with the amount of available experimental data on sub ∆ Htypically sublimation data are known for thousands of compounds while millions of crystal structures have been resolved. 9 Therefore, a reliable and generally applicable computational methodology capable of predicting sub ∆ H from the first principles would certainly find many uses in the future.…”

Off-the-shelf DFT-DISPersion methods: Are they now “on-trend” for organic molecular crystals?

“…After all crystal structures were associated with a single sublimation enthalpy datapoint, the experimental lattice energy ( ) was estimated from the (average) sublimation enthalpy ( ) using the following approximate relationship 2,25 , where is the molar gas constant and is the temperature in Kelvin, which ranged from 223 -455 Kelvin, across different dataset entries:…”

“…In the pharmaceutical industry, the ADDoPT project (Advanced Digital Design of Pharmaceutical Therapeutics: https://www.addopt.org/about_addopt/) has combined expertise from industry, academia and small-to-medium enterprises (SMEs) in a combined effort to digitalize the tablet-production pipeline, from the atomistic scale of single molecules to molecular crystals to macroscopic bulkscale tablets. Both classical and first-principles atomistic simulations of organic molecular crystals play a role in this pipeline, where their calculated lattice energies could inform structure, performance, properties and processing 1 , such as thermodynamic solubility [2][3][4][5] , stability [6][7][8] , and crystallization [9][10][11] as well as crystal structure prediction 12,13 . Lattice energies can be calculated using classical, atomistic methods [14][15][16][17][18][19][20] , i.e.…”

Ab Initio Prediction of Stable Crystal Structure of Procarbazine Molecule

“…The packing density and the unit cell volume of each generated structure was analyzed with PMIN refinement [8] using a repulsion only UMD potential [16] and optimize the packing arrangements in the commonly encountered space groups P1, P-1, P2, Pm, Pc, P2 1 , P2/c, P2 1 /m, P2/m, P2 1 /c, Cc, C2, C2/c, Pnn2, Pba2, Pnc2, P22 1 , Pmn2 1 , Pma2, P2 1 2 1 2 1 , P2 1 2 1 2, Pca2 1 , Pna2 1, Pnma and Pbca. The PMIN optimized densest structures were exposed to the inner lattice minimization using the DMACRYS algorithm [9].…”