Uncertainty Quantification of Linear Scaling, Machine Learning, and Density Functional Theory Derived Thermodynamics for the Catalytic Partial Oxidation of Methane on Rhodium

Accurate and complete microkinetic models (MKMs) are
powerful for
anticipating the behavior of complex chemical systems at different
operating conditions. In heterogeneous catalysis, they can be further
used for the rapid development and screening of new catalysts. Density
functional theory (DFT) is often used to calculate the parameters
used in MKMs with relatively high fidelity. However, given the high
cost of DFT calculations for adsorbates in heterogeneous catalysis,
linear scaling relations (LSRs) and machine learning (ML) models were
developed to give rapid estimates of the parameters in MKM. Regardless
of the method, few studies have attempted to quantify the uncertainty
in catalytic MKMs, as the uncertainties are often orders of magnitude
larger than those for gas phase models. This study explores uncertainty
quantification and Bayesian Parameter Estimation for thermodynamic
parameters calculated by DFT, LSRs, and GemNet-OC, a ML model developed
under the Open Catalyst Project. A model for catalytic partial oxidation
of methane (CPOX) on Rhodium was chosen as a case study, in which
the model’s thermodynamic parameters and their associated uncertainties
were determined using DFT, LSR, and GemNet-OC. Markov Chain Monte
Carlo coupled with Ensemble Slice Sampling was used to sample the
highest probability density (HPD) region of the posterior and determine
the maximum of the a posteriori (MAP) for each thermodynamic parameter
included. The optimized microkinetic models for each of the three
estimation methods had quite similar mechanisms and agreed well with
the experimental data for gas phase mole fractions. Exploration of
the HPD region of the posterior further revealed that adsorbed hydroxide
and oxygen likely bind on facets other than Rhodium 111. The demonstrated
workflow addresses the issue of inaccuracies arising from the integration
of data from multiple sources by considering both experimental and
computational uncertainties, and further reveals information about
the active site that would not have been discovered without considering
the posterior.