TS-tools: Rapid and Automated Localization of Transition States based on a Textual Reaction SMILES Input

09 April 2024, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


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.


transition states

Supplementary materials

Supporting Information
Cleaned up version of the mono-/bimolecular reaction benchmarking dataset, settings of the TS searches performed, transition state searches with failed validation, reaction profiles for the reaction between toluene and Cl2 in apolar environments, note about the modeling of the solvated Passerini reaction. The main code used to generate the presented results can be found at https://github.com/chimie-paristech-CTM/TS-tools. The final gaussian16 outputs of all electronic structure calculations, as well as .xyz files for reactive complexes and optimized TSs, can be downloaded from https://doi.org/10.6084/m9.figshare.25043918.


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