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

Johnson, Matthew S.; Bross, David H.; Zádor, Judit

doi:10.1021/acs.jpcc.4c06636

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

Journal Article · Thu Feb 06 23:00:00 EST 2025 · Journal of Physical Chemistry. C

DOI:https://doi.org/10.1021/acs.jpcc.4c06636· OSTI ID:2574983

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Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States)

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. In conclusion, 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); USDOE National Nuclear Security Administration (NNSA); National Energy Research Scientific Computing Center (NERSC)

Grant/Contract Number:: NA0003525; AC02-06CH11357

OSTI ID:: 2574983

Journal Information:: Journal of Physical Chemistry. C, Journal Name: Journal of Physical Chemistry. C Journal Issue: 7 Vol. 129; ISSN 1932-7455; ISSN 1932-7447

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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