Evaluation of Rate Coefficients in the Gas Phase Using Machine-Learned Potentials

We assess the capability of machine-learned potentials
to compute
rate coefficients by training a neural network (NN) model and applying
it to describe the chemical landscape on the C5H5 potential energy surface, which is relevant to molecular weight
growth in combustion and interstellar media. We coupled the resulting
NN with an automated kinetics workflow code, KinBot, to perform all
necessary calculations to compute the rate coefficients. The NN is
benchmarked exhaustively by evaluating its performance at the various
stages of the kinetics calculations: from the electronic energy through
the computation of zero point energy, barrier heights, entropic contributions,
the portion of the PES explored, and finally the overall rate coefficients
as formulated by transition state theory.