pyMCD: Python package for searching transition states via the multicoordinate driven method

Published: 19 July 2023| Version 1 | DOI: 10.17632/wb6r97mb5s.1


Elucidation of activation barriers is essential to understand the energetics of chemical reactions. Transition states (TSs) are the key information necessary to evaluate activation barriers. Among various methods, the multicoordinate driven (MCD) method is particularly useful for searching TSs because it requires simple inputs but shows high reliability with reasonable computation costs. This method starts from reactants and generates a reaction path by scanning multiple active coordinates until it arrives at the products, eventually leading to a TS and the corresponding activation barrier. Despite its high reliability, however, the source code is not publicly available. Herein, we present a Python package, hereafter referred to as pyMCD that searches for TSs using the MCD method. We slightly revised the original MCD method proposed by Berente and coworkers [1] to improve its computational efficiency while minimizing loss of accuracy. The package is extremely user-friendly, requiring minimal effort from users for input preparation. Moreover, it is well organized, so users can readily customize it for their purposes. The current version has been interfaced with Gaussian and ORCA, but can be interfaced with any quantum chemistry package by slightly modifying the source code. We demonstrated the high reliability of the revised method by testing it with various chemical reactions.



Molecular Physics, Computational Physics, Chemical Reaction