  The ab initio calculation of exact quantum reaction rate constants comes at a
high cost due to the required dynamics of reactants on multidimensional
potential energy surfaces. In turn, this impedes the rapid design of the
kinetics for large sets of coupled reactions. In an effort to overcome this
hurdle, a deep neural network (DNN) was trained to predict the logarithm of
quantum reaction rate constants multiplied by their reactant partition function
- rate products. The training dataset was generated inhouse and contains ~1.5
million quantum reaction rate constants for single, double, symmetric and
asymmetric one-dimensional potentials computed over a broad range of reactant
masses and temperatures. The DNN was able to predict the logarithm of the rate
product with a relative error of 1.1%. Further, when comparing the difference
between the DNN prediction and classical transition state theory at
temperatures below 300K a relative percent error of 31% was found with respect
to the exact difference. Systems beyond the test set were also studied, these
included the $\sf{H}$ + $\sf{H_2}$ reaction, the diffusion of hydrogen on
Ni(100), the Menshutkin reaction of pyridine with $\sf{CH_3Br}$ in the gas
phase, the reaction of formalcyanohydrin with $\sf{HS^-}$ in water and the F +
HCl reaction. For these reactions, the DNN predictions were accurate at high
temperatures and in good agreement with the exact rates at lower temperatures.
This work shows that one can take advantage of a DNN to gain insight on
reactivity in the quantum regime.
