Group Equivalent Machine Learning Approach to Predict Hydrocarbon Strain Energy

Strain energy is an important property in molecules intended for reactive and energetic applications. We present a machine learning approach to predict hydrocarbon strain energies using Benson group equivalents. An algorithm is developed to break down hydrocarbons into their group equivalent components and a combined featurization strategy is developed using the group equivalents and other simple physicochemical features. The training data are obtained from a limited number of quantum chemistry simulations. A machine learning approach to predict hydrocarbon molecule strain energies is then described and evaluated.