Given the limitation of LSRs, ML models have been developed to estimate the molecular or atomic energy in a wider range of materials. There are various ML models to help accelerate heterogeneous catalysis modeling. This includes ML-aided potential energy surface construction, atomistic structure and potential estimation, and material designs (finding the optimal composition of materials). Both kinetics and thermodynamics evaluations in MKM can substantially benefit from ML models. In this study, ML-predicted thermodynamics are used. A neural network named GemNet-OC, developed under Open Catalyst Project (OCP), was picked to carry out the task. GemNet-OC has been demonstrated to have good accuracy for energy and force estimation, and has been used in several works. DFT, LSRs, and ML models all have uncertainty associated with their predictions of species properties, which can be propagated forward through a MKM to reveal the uncertainty in quantities of interest such as turnover frequency, conversion, selectivity, etc. Previous studies have highlighted the importance of propagating energetic parameter uncertainties to industrial operating conditions. Beyond simply determining a "most probable" mechanism, analyzing possible reaction pathways within a given uncertainty space can reveal entirely di↵erent reaction pathways and active sites. Bayes theorem proposes a method for incorporating the prior uncertainties of a model and the marginal probability of observed data as a means of generating a most probable set of parameters. The process for obtaining these credible values is referred to as Bayesian Parameter Estimation (BPE). BPE can also be used to generate the highest probability density (HPD) region around the most likely parameters, which in essence is a probability distribution that has been informed both by prior knowledge and experimental data. This requires comprehensively exploring the parameter space within the model, which can be di cult for a complex system with a large number of parameters. Various methods can be used to e ciently explore the uncertainty space, including surrogate models like polynomial chaos expansion, 37 grid based approaches, and Monte Carlo simulation based methods. Monte Carlo methods are expensive, but they are applicable to almost any model, and can be made more e cient through sophisticated sampling methods. Markov Chain Monte Carlo (MCMC) is particularly useful for model uncertainty quantification, because it implements a probability based approach to sampling, where the "jump" to the next sampled point is based on the relative probability of the previous point. BPE coupled with MCMC has been used successfully for surface chemistry models already in several studies, proving that it is a robust, albeit expensive, method for generating posteriors for catalytic systems. In this study, BPE was performed via MCMC sampling to reveal posterior uncertainties in both the input species binding energies and the output molar flow rates. Quantifying the uncertainties in models generated using di↵erent thermodynamic calculation method (DFT, LSRs, and GemNet model) provided a more informed basis for comparing them than simply analyzing the initial, unoptimized models.