A Machine Learning Approach for Rate Constants. II. Clustering, Training, and Predictions for the O(³P) + HCl → OH +...

Following up on our recent paper, which reported a machine learning approach to train on and predict thermal rate constants over a large temperature range, we present new results by using clustering and new Gaussian process regression on each cluster. Each cluster is defined by the magnitude of the correction to the Eckart transmission coefficient. Instead of the usual protocol of training and testing, which is a challenge for present small database of exact rate constants, training is done on the full data set for each cluster. Testing is done by inputing hundreds of random values of the descriptors (within reasonable bounds). The new training strategy is applied to predict the rate constants of the O(³P) + HCl reaction on the ³A' and ³A″ potential energy surfaces. This reaction was recently focused on as a "stress test" for the ring polymer molecular dynamics method. Finally, this reaction is added to the databases and training is done with this addition. The freely available database and new Python software that evaluates the correction to the Eckart transmission coefficient for any reaction are briefly described.