  Simulations of chemical reaction probabilities in gas surface dynamics
require the calculation of ensemble averages over many tens of thousands of
reaction events to predict dynamical observables that can be compared to
experiments. At the same time, the energy landscapes need to be accurately
mapped, as small errors in barriers can lead to large deviations in reaction
probabilities. This brings a particularly interesting challenge for machine
learning interatomic potentials, which are becoming well-established tools to
accelerate molecular dynamics simulations. We compare state-of-the-art machine
learning interatomic potentials with a particular focus on their inference
performance on CPUs and suitability for high throughput simulation of reactive
chemistry at surfaces. The considered models include polarizable atom
interaction neural networks (PaiNN), recursively embedded atom neural networks
(REANN), the MACE equivariant graph neural network, and atomic cluster
expansion potentials (ACE). The models are applied to a dataset on reactive
molecular hydrogen scattering on low-index surface facets of copper. All models
are assessed for their accuracy, time-to-solution, and ability to simulate
reactive sticking probabilities as a function of the rovibrational initial
state and kinetic incidence energy of the molecule. REANN and MACE models
provide the best balance between accuracy and time-to-solution and can be
considered the current state-of-the-art in gas-surface dynamics. PaiNN models
require many features for the best accuracy, which causes significant losses in
computational efficiency. ACE models provide the fastest time-to-solution,
however, models trained on the existing dataset were not able to achieve
sufficiently accurate predictions in all cases.
