TS‐tools: Rapid and automated localization of transition states based on a textual reaction SMILES input

Abstract

Here, TS‐tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono‐ and bimolecular reactions, TS‐tools reaches an excellent success rate of 95% already at xTB level of theory. For tri‐ and multimolecular reaction pathways ‐ which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent‐ and autocatalysis and enzymatic reactivity ‐ TS‐tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation‐induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.