When Noise Helps: Probabilistic Kinetic Solving for Reaction Networks

Predicting the outcome of a chemical reaction from first principles would let us screen molecules, materials and drugs on the computer instead of in the lab. Reaction networks, which map out the possible pathways a reaction can take, are a standard tool for this. A kinetic solver turns a network into a reaction outcome, but its reaction rates depend exponentially on the energy barriers computed by quantum chemistry. Even small barrier errors, within reach of the most expensive methods, can flip the predicted product. We introduce a kinetic solver that treats barriers as distributions instead of fixed numbers, and predicts the expected outcome over that uncertainty. This gives more accurate and more reproducible predictions, at a cost that shrinks as uncertainty grows. We validate these gains on synthetic networks spanning a wide range of topologies, temperatures, and reaction times, and qualitatively on real reaction networks from the literature. As machine learning pushes reaction network exploration towards full automation, manual error curation becomes infeasible, and uncertainty aware solvers offer a path to reliable chemistry prediction at scale.