Unveiling the reaction pathways of hydrocarbons via experiments, computations and data science

Abstract

Reaction networks of hydrocarbons are explored using first principles calculations, data science, and experiments. Transforming hydrocarbon data into networks reveals the prevalence of the formation and reaction of various molecules. Graph theory is implemented to extract knowledge from the reaction network. In particular, centralities analysis reveals that H⁺, CCC, CH₃⁺, CC, and [CH₂⁺]C have high degrees and are thus very likely to form or react with other molecules. Additionally, H⁺, CH₃⁺, C₂H₅⁺, C₈H₁₅⁺, C₈H₁₇⁺, and C₆H₁₁⁺ are found to have high control throughout the network and lead towards a series of additional reactions. The constructed network is also validated in experiments while the shortest path analysis is implemented for further comparison between experiment and the network. Thus, combining network analysis with first principles calculations uncovers key points in the development of various hydrocarbons that can be used to improve catalyst design and targeted synthesis of desired hydrocarbons.