Feasibility of Activation Energy Prediction of Gas‐Phase Reactions by Machine Learning

Machine learning based on big data has emerged as a powerful solution in various chemical problems. We investigated the feasibility of machine learning models for the prediction of activation energies of gas‐phase reactions. Six different models with three different types, including the artificial neural network, the support vector regression, and the tree boosting methods, were tested. We used the structural and thermodynamic properties of molecules and their differences as input features without resorting to specific reaction types so as to maintain the most general input form for broad applicability. The tree boosting method showed the best performance among others in terms of the coefficient of determination, mean absolute error, and root mean square error, the values of which were 0.89, 1.95, and 4.49 kcal mol−1, respectively. Computation time for the prediction of activation energies for 2541 test reactions was about one second on a single computing node without using accelerators. Activation energy prediction: The performance of various machine learning models for prediction of activation energies of gas‐phase reactions was examined. Fast prediction with desirable accuracy was feasible by using only the thermodynamic and structural properties of reactants and products without knowing the reaction paths. The tree boosting method showed the best performance (mean absolute error=1.95 kcal mol−1) over the artificial neural network and supporting vector regression methods.