A minimal subnetwork is extracted from a very complex full network upon exploring the reaction pathways connecting reactants and products with minimum dissociation and formation of chemical bonds. Such a process reduces computational cost and correctly predicts the pathway for two representative reactions.

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 , 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.