Predicting the Outcome of Photocyclisation Reactions: A Joint Experimental and Computational Investigation

Photochemical oxidative cyclodehydrogenation reactions are a versatile class of aromatic ring‐forming reactions. They are tolerant to functional group substitution and heteroatom inclusion, so can be used to form a diverse range of extended polyaromatic systems by fusing existing ring substituents. However, despite their undoubted synthetic utility, there are no existing models—computational or heuristic—that predict the outcome of photocyclisation reactions across all possible classes of reactants. This can be traced back to the fact that “negative” results are rarely published in the synthetic literature and the lack of a general conceptual framework for understanding how photoexcitation affects reactivity. In this work, we address both of these issues. We present experimental data for a series of aromatically substituted pyrroles and indoles, and show that quantifying induced atomic forces upon photoexcitation provides a powerful predictive model for determining whether a given reactant will photoplanarise and hence proceed to photocyclised product under appropriate reaction conditions. The propensity of a molecule to photoplanarise is related to localised changes in charge distribution around the putative forming ring upon photoexcitation. This is promoted by asymmetry in molecular structures and/or charge distributions, inclusion of heteroatoms and ethylene bridging and well‐separated or isolated photocyclisation sites. Stop being so predictable! A photo‐induced force‐based reactivity predictor is developed to predict the outcome of photocyclisation reactions. It is based upon analysis of photo‐induced atomic forces obtained from quantum chemical calculations. It is tested on a range of potentially photocyclisable systems, benchmarking against experimental observations both from the literature and our new work presented here. Our new reactivity predictor is accurate, powerful and generally applicable, and produces useful insights to help guide future experimental work.