Data Science Approach to Estimate Enthalpy of Formation of Cyclic Hydrocarbons

In
spite of increasing importance of cyclic hydrocarbons in various
chemical systems, studies on the fundamental properties of these compounds,
such as enthalpy of formation, are still scarce. One of the reasons
for this is the fact that the estimation of the thermodynamic properties
of cyclic hydrocarbon species via cost-effective computational approaches,
such as group additivity (GA), has several limitations and challenges.
In this study, a machine learning (ML) approach is proposed using
a support vector regression (SVR) algorithm to predict the standard
enthalpy of formation of cyclic hydrocarbon species. The model is
developed based on a thoroughly selected dataset of accurate experimental
values of 192 species collected from the literature. The molecular
descriptors used as input to the SVR are calculated via alvaDesc software,
which computes in total 5255 features classified into 30 categories.
The developed SVR model has an average error of approximately 10 kJ/mol.
In comparison, the SVR model outperforms the GA approach for complex
molecules and can be therefore proposed as a novel data-driven approach
to estimate enthalpy values for complex cyclic species. A sensitivity
analysis is also conducted to examine the relevant features that play
a role in affecting the standard enthalpy of formation of cyclic species.
Our species dataset is expected to be updated and expanded as new
data are available to develop a more accurate SVR model with broader
applicability.