The concurrent learning algorithm outlined above generates a set of TS geometries for each elementary step through various iterations of the rMLP model. To ensure that these TS guesses correspond to the correct reactions, we developed a multi-step auditing protocol (Figure ). This TS auditing stage automatically analyzes a set of MLP-optimized transition states based on several criteria to confirm that the most stable (i.e., lowest energy) and valid TS structure is used for further refinement in Stage 3. In addition to verifying that each TS has a single imaginary frequency, we confirm that the MLP/TS connects the intended reactants and products. Specifically, the adjacency lists of the reactants and products supplied to Pynta are automatically compared against the endpoints of the IRC. This is done by converting these IRC endpoints into a two-dimensional representation and performing a graph isomorphism test with the Pynta input. This tool was built upon previous tools developed to study coverage dependence 71 within Pynta. An example of the TS auditing algorithm is provided in Supplementary Information (Figure ).