The first goal of this study was to analyze the uncertainty inherent in di↵erent thermodynamic estimation methods for adsorbed species. It is clear that the ML model is on par with the LSR and DFT models, with the additional benefit that the thermodynamics did not require any expensive DFT calculations up front, which cannot be said for the other two models. Knowing the uncertainty of the predictions from a calculator before using it to screen thousands of catalysts is vital for any method. Given the large shifts from the prior values to the MAP values for all 3 models, the apparent increase in uncertainty for the ML model is made up for by its speed compared to DFT and universality compared to LSRs. Further, for the system at hand, we showed that while supplying covariance data was useful for having slightly more accurate posteriors, it did not significantly change the final MAP values or the optimized models. This is encouraging, as the data necessary to determine covariance are not always available, especially when dealing with mechanisms that use data from a variety of sources, for example those generated by the Reaction Mechanism Generator.