Chemical Reaction Networks from Scratch with Reaction Prediction and Kinetics-Guided Exploration
Michael Woulfe, Brett M. Savoie
Abstract
Algorithmic reaction explorations based on transition state searches can now routinely predict relatively short reaction sequences involving small molecules. However, applying these algorithms to deeper chemical reaction network (CRN) exploration still requires the development of more efficient and accurate exploration policies. Here, an exploration algorithm, which we name yet another kinetic strategy (YAKS), is demonstrated that uses microkinetic simulations of the nascent network to achieve cost-effective, deep network exploration. Key features of the algorithm are the automatic incorporation of bimolecular reactions between network intermediates, compatibility with short-lived but kinetically important species, and incorporation of rate uncertainty into the exploration policy. In validation case studies of glucose pyrolysis, the algorithm rediscovers reaction pathways previously discovered by heuristic exploration policies and elucidates new reaction pathways for experimentally obtained products. The resulting CRN is the first to connect all major experimental pyrolysis products to glucose. Additional case studies are presented that investigate the role of reaction rules, rate uncertainty, and bimolecular reactions. These case studies show that naïve exponential growth estimates can vastly overestimate the actual number of kinetically relevant pathways in the physical reaction networks. In light of this, further improvements in exploration policies and reaction prediction algorithms make it feasible that CRNs might soon be routinely predictable in some contexts.