Predicting atomic-level reaction mechanisms for S_N2 reactions via machine learning

Identifying atomic-level reaction mechanisms is an essential step in chemistry. In this study, we develop a joint-voting model based on three parallel machine-learning algorithms to predict atomic-level and dynamical mechanisms trained with 1700 trajectories. Three predictive experiments are carried out with the training trajectories divided into ten, seven, and five classes. The results indicate that, as the number of trajectories in each class increases from the ten- to five-class model, the five-class model converges the fastest and the prediction success rate increases. The number of trajectories in each experiment to get the predictive models converged is 100, 100, and 70, respectively. The prediction accuracy increases from 88.3% for the ten-class experiment, to 91.0% for the seven-class, and to 92.0% for the five-class. Our study demonstrates that machine learning can also be used to predict elementary dynamical processes of structural evolution along time, that is, atomic-level reaction mechanisms.