Accelerating the identification of the rate controlling steps by conducting microkinetic modeling on surrogate networks

Identifying the rate-controlling steps in an unknown reaction network can be time-consuming due to its inherent complexity. Here we present a strategy to simplify this process by focusing expensive barrier calculations on significant elementary steps. The strategy is constructed by determining significant elementary steps using the degree of rate control data, which is derived from microkinetic modeling calculations performed on surrogate networks, which are a series of networks generated by assigning fictitious values to unknown barriers while all the reaction energies are computed using density functional theory. The barriers for significant elementary steps are then calculated iteratively to refine the network. We demonstrate this strategy for the reaction of Fischer–Tropsch synthesis, which has already been extensively studied in our previous work. Applying the strategy, we identified the most rate-controlling step, achieving a 77% reduction in the number of transition state calculations compared to traditional methods. Additionally, a detailed analysis of the strategy reveals the correlation between the parameters in the strategy and its performance. We validate the practicability of the strategy by applying it onto testing networks and the potential limitations of the strategy are also discussed.