A single chemical reaction can have more than one destination, even after molecules pass through what appears to be the same critical energy barrier. New research from Saitama University offers a computational way to expose those hidden routes, potentially giving chemists a clearer map of why reactions produce unexpected mixtures.
The study, Application of the String Method to Determine the Ambimodal Nature of Transition States in Bifurcation Reactions, examines a phenomenon called post-transition-state bifurcation. In these reactions, one transition state can lead to two or even three products rather than following a single downhill track. The new calculations reveal hidden chemical pathways that standard reaction-path searches can overlook.
Researchers led by Professor Toshiyuki Takayanagi, with Keita Yamaki as first author, tested the approach across 33 reaction systems. Their method uses a double-ended string calculation to optimize a route between two molecular structures, allowing the team to investigate a possible connection between the same transition state and an alternative product. Machine-learning energy estimates were combined with quantum-chemical calculations to determine whether that route remained downhill or required a smaller additional barrier.
That distinction matters because conventional intrinsic reaction-coordinate calculations generally follow the path from a transition state toward the nearest product. Molecular-dynamics simulations can reveal branching, but they may require hundreds or thousands of individual trajectories, making them expensive for routine mechanistic studies. The string-based workflow instead gives researchers a practical way to search for overlooked connections before applying more demanding simulations.
The results do not predict the exact percentage of each product a reaction will generate. They map the available energy landscape: whether an alternative product is connected to the same transition state and whether the route is energetically accessible. In that sense, the technique complements molecular dynamics rather than replacing it, while helping explain why a reaction can defy the simplified assumption that one transition state leads to one outcome.
The paper appeared online September 28, 2026, in the Journal of Computational Chemistry. A fuller picture of competing pathways could eventually help chemists design more selective processes for pharmaceuticals, fine chemicals, and advanced materials. The Saitama team’s next logical step is to combine the method with automated reaction searches and molecular-dynamics simulations, turning hidden branching routes into information that can guide real-world synthesis.
