Automated Reaction Discovery from Combined Application of Transition State Search Algorithms

With petascale computers, new chemistry can be discovered more rapidly in silico than in the laboratory. In this work, we investigate the unimolecular decomposition of γ-ketohydroperoxide using computational schemes that automatically search for elementary reaction steps, i.e., a combination of the Berny optimization algorithm with the freezing string method, the single- and double-ended growing string methods, the heuristic KinBot algorithm, and the single-component artificial force induced reaction method (SC-AFIR). These efforts lead to a discovery of 75 elementary unimolecular reactions of γ-ketohydroperoxide, 69 of which were previously unknown. All of the methods we adopted found the lowest energy transition state corresponding to the first step of the Korcek mechanism. However, each algorithm except for SC-AFIR discovered several reactions not detected by any of the other methods. The present work demonstrates both the strengths and weaknesses of existing schemes for automated reaction discovery and highlights the advantage of the combined application of several computational approaches. However, for the reliable discovery of all important reactions of any given reactants, further method development and assessment is required.