Capturing the Crystal:
Prediction of Enthalpy of Sublimation,
Crystal Lattice Energy, and Melting Points of Organic Compounds
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Abstract
Accurate computational prediction of melting points and
aqueous
solubilities of organic compounds would be very useful but is notoriously
difficult. Predicting the lattice energies of compounds is key to
understanding and predicting their melting behavior and ultimately
their solubility behavior. We report robust, predictive, quantitative
structure–property relationship (QSPR) models for enthalpies
of sublimation, crystal lattice energies, and melting points for a
very large and structurally diverse set of small organic compounds.
Sparse Bayesian feature selection and machine learning methods were
employed to select the most relevant molecular descriptors for the
model and to generate parsimonious quantitative models. The final
enthalpy of sublimation model is a four-parameter multilinear equation
that has an r<sup>2</sup> value of 0.96 and an average absolute error
of 7.9 ± 0.3 kJ.mol<sup>–1</sup>. The melting point model
can predict this property with a standard error of 45° ±
1 K and r<sup>2</sup> value of 0.79. Given the size and diversity
of the training data, these conceptually transparent and accurate
models can be used to predict sublimation enthalpy, lattice energy,
and melting points of organic compounds in general