# Machine Learning Quantum Reaction Rate Constants

journal contribution

posted on 30.09.2020, 20:15 by Evan Komp, Stéphanie ValleauThe 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 in-house 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%. Furthermore,
when comparing the difference between the DNN prediction and classical
transition state theory at temperatures below 300 K 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 H + H

_{2}reaction, the diffusion of hydrogen on Ni(100), the Menshutkin reaction of pyridine with CH_{3}Br in the gas phase, the reaction of formalcyanohydrin with 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.## Read the peer-reviewed publication

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## Keywords

reactant massesMenshutkin reactionDNN predictionHSCH 3 Brmachine Learning Quantum Reaction R...training datasetHCl reactiongas phasepercent errorenergy surfacesab initio calculation300 Kgain insightDNN predictionsquantum regimetransition state theoryquantum reaction rate constantsH 2 reactionrate product

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## Read the peer-reviewed publication

## Categories

## Keywords

reactant massesMenshutkin reactionDNN predictionHSCH 3 Brmachine Learning Quantum Reaction R...training datasetHCl reactiongas phasepercent errorenergy surfacesab initio calculation300 Kgain insightDNN predictionsquantum regimetransition state theoryquantum reaction rate constantsH 2 reactionrate product