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.