Deep Reaction Network Exploration of Glucose Pyrolysis

Resolving the reaction networks associated with biomass pyrolysis is central to understanding product selectivity and aiding catalyst design to produce more valuable products. However, even the pyrolysis network of relatively simple $\beta$-D-Glucose remains unresolved due to its significant complexity in terms of the depth of the network and the number of major products. Here, a transition-state guided reaction exploration has been performed that provides complete pathways to all significant experimental pyrolysis products of $\beta$-D-Glucose. The resulting reaction network involves over 31,000 reactions and transition states computed at the semi-empirical quantum chemistry level and approximately 7,000 kinetically relevant reactions and transition states characterized at the DFT level, comprising the largest reaction network reported for biomass pyrolysis. The exploration was conducted using graph-based rules to explore the reactivities of intermediates and an adaption of Dijkstra algorithm to identify kinetically relevant intermediates. This simple exploration policy surprisingly (re)discovered pathways to all major experimental pyrolysis products, many intermediates proposed by previous computational studies, and also identified new low-barrier reaction mechanisms that resolve outstanding discrepancies between reaction pathways and yield in isotope labeling experiments. This network also provides explanatory pathways for the high yield of hydroxymethylfurfural (HMF) and the reaction pathway that contributes most to the formation of hydroxyacetaldehyde (HAA) during glucose pyrolysis. Due to the limited domain knowledge required to generate this network, this approach should also be transferable to other complex reaction network prediction problems in biomass pyrolysis.