Machine Learning To Predict Standard Enthalpy of Formation of Hydrocarbons

Thermodynamic properites
of molecules are used widely in the study
of reactive processes. Such properties are typically measured via
experiments or calculated by a variety of computational chemistry
methods. In this work, machine learning (ML) models for estimation
of standard enthalpy of formation at 298.15 K are developed for three
classes of acyclic and closed-shell hydrocarbons, viz. alkanes, alkenes,
and alkynes. Initially, an extensive literature survey is performed
to collect standard enthalpy data for training ML models. A commercial
software (Dragon) is used to obtain a wide set of molecular descriptors
by providing SMILES strings. The molecular descriptors are used as
input features for the ML models. Support vector regression (SVR)
and artificial neural networks are used with a two-level K-fold cross-validation
(K-fold CV) workflow. The first level is for estimation of accuracy
of both the ML models, and the second level is for generation of the
final models. The SVR model is selected as the best model based on
error estimates over 10-fold CV. The final SVR model is compared against
conventional Benson’s group additivity for a set of octene
isomers from the database, illustrating the advantages of the proposed
ML modeling approach.