In summary, this work has shown that, with particular care, catalytic reactions in amorphous heterogeneous materials can be accurately described with DFT simulations on minimalistic cluster models. However, one needs to be aware when building kinetics models that multiple TS structures can be present on the same active site with significantly different adsorption energies and reaction barriers. Machine learning models can provide useful qualitative insights on the main descriptors for the reactive event even with small data sets. Quantitative ML models require large training data set, which we can now compile by creating relative inexpensive Si-Min models. Therefore, future studies will focus on quantitative simulations of effective reaction rates that are directly comparable to experiment. figure with the average RMSE of ∆E tot ads with all possible combinations of descriptors (with ϕ ij as a constant) over repeated 100 random trials.