The effectiveness of this Pynta-based workflow is demonstrated on a diverse set of 53 reactions (Table ) described by Aljama et al. to generate an MKM for Ag-catalyzed methanol oxidation, an important industrial process for producing the commodity chemical formaldehyde. We demonstrate a total model approach whereby we train a single model simultaneously on the set of these 53 reactions in parallel. Additionally, we also explore a reaction-class-based approach which constructs a model for seven distinct reaction classes (see Table ) which can take advantage of information sharing, previously demonstrated to improve MLP performance. Together, this rMLP-accelerated Pynta approach provides accurate transition state geometries for all 53 reactions at a fraction of the computational cost compared to comprehensive DFT/NEB-based approaches. Our analyses show that the Pynta-based curriculum is seven times cheaper than using Pynta/DFT. With the automated generation of thermochemistry and kinetic libraries for updating RMG, this means the reaction network can be easily expanded to include reactions not present in the initial reaction dataset. Our concurrent learning scheme also facilitates using any MLP architecture.