  We have generated an open-source dataset of over 30000 organic chemistry gas
phase partition functions. With this data, a machine learning deep neural
network estimator was trained to predict partition functions of unknown organic
chemistry gas phase transition states. This estimator only relies on reactant
and product geometries and partition functions. A second machine learning deep
neural network was trained to predict partition functions of chemical species
from their geometry. Our models accurately predict the logarithm of test set
partition functions with a maximum mean absolute error of 2.7%. Thus, this
approach provides a means to reduce the cost of computing reaction rate
constants ab initio. The models were also used to compute transition state
theory reaction rate constants prefactors and the results were in quantitative
agreement with the corresponding ab initio calculations with an accuracy of
98.3% on the log scale.
