Learning Molecular Representations for Thermochemistry Prediction of Cyclic Hydrocarbons and Oxygenates

Accurate
thermochemistry estimation of polycyclic molecules is
crucial for kinetic modeling of chemical processes that use renewable
and alternative feedstocks. In kinetic model generators, molecular
properties are estimated rapidly with group additivity, but this method
is known to have limitations for polycyclic structures. This issue
has been resolved in our work by combining a geometry-based molecular
representation with a deep neural network trained on ab initio data. Each molecule is transformed into a probabilistic vector from
its interatomic distances, bond angles, and dihedral angles. The model
is tested on a small experimental dataset (200 molecules) from the
literature, a new medium-sized set (4000 molecules) with both open-shell
and closed-shell species, calculated at the CBS-QB3 level with empirical
corrections, and a large G4MP2-level QM9-based dataset (40 000
molecules). Heat capacities between 298.15 and 2500 K are calculated
in the medium set with an average deviation of about 1.5 J mol–1 K–1 and the standard entropy at
298.15 K is predicted with an average error below 4 J mol–1 K–1. The standard enthalpy of formation at 298.15
K has an average out-of-sample error below 4 kJ mol–1 on a QM9 training set size of around 15 000 molecules. By
fitting NASA polynomials, the enthalpy of formation at higher temperatures
can be calculated with the same accuracy as the standard enthalpy
of formation. Uncertainty quantification by means of the ensemble
standard deviation is included to indicate when molecules that are
on the edge or outside of the application range of the model are evaluated.