Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows

Microkinetic models for catalytic systems require estimation
of
many thermodynamic and kinetic parameters that can be calculated for
isolated species and transition states using ab initio methods. However,
the presence of nearby coadsorbates on the surface can dramatically
alter these thermodynamic and kinetic parameters causing them to be
dependent on species coverage fractions. As there are combinatorially
many coadsorbed configurations on the surface, computing the coverage
dependence of these parameters is far less straightforward. We present
a framework for generating and applying machine learning models to
predict coverage-dependent parameters for microkinetic models. Our
toolkit enables automatic calculation and evaluation of coadsorbed
configurations allowing us to sample 2,000 coadsorbed adsorbates and
transition states (TSs) for a diverse set of 9 reactions on Cu(111),
a challenging surface, with four possible coadsorbates. This dataset
was then used to train subgraph isomorphic decision trees (SIDTs)
to predict the stability and association energy of configurations.
We were able to achieve mean absolute errors (MAEs) of 0.106 eV on
adsorbates, 0.172 eV on TSs, and due to natural error cancellation
in SIDTs for relative properties, 0.130 eV on reaction energies and
0.180 eV on activation barriers. We describe how to use these models
to predict coverage-dependent corrections for adsorbates and TSs and
demonstrate on H*, HO*, and O* comparing
the generated SIDT model with an iteratively refined version.