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.Program summary Program Title: pyMCDCPC Library link to program files: https://doi .org /10 .17632 /wb6r97mb5s .1Developer's repository link: https://github .com /kyunghoonlee777 /pyMCD .gitLicensing provisions: BSD-3-clauseProgramming language: PythonSupplementary material:1. Input files for the results section (input_files.tar)2. Output files of the results section (output_files.tar)3. Manual for installing and running pyMCD (manual.pdf)Nature of problem: Rapid and reliable transition state (TS) exploration is challenging because TSs are saddle points on the potential energy surface of chemical reactions. The multicoordinate driven (MCD) method was proposed as a powerful approach for TS searching with various advantages, including high reliability and convenience of its use. Unfortunately, neither the source code nor any executable version of it is publicly available.Solution method: We provide a Python package, namely, pyMCD, that can search TSs using the MCD method. The method was implemented in Python with high-performance and user-friendly environments. Our test study demonstrated the high reliability of our implementation. The code structure is well organized, so users can easily customize it for their purposes. Moreover, it can be readily interfaced with any quantum chemistry software with minimal effort. At present, it has been interfaced with Gaussian and ORCA.& COPY; 2023 Elsevier B.V. All rights reserved.
