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Related Concept Videos

SN2 Reaction: Transition State02:26

SN2 Reaction: Transition State

An SN2 reaction of an alkyl halide is a single-step process in which bond formation between the nucleophile and the substrate and bond breaking between the substrate and the halide occurs simultaneously through a transition state without forming an intermediate.
When the nucleophile approaches the electrophilic carbon with its lone pairs, the halide acts as a leaving group and moves away with the electron-pair bonded to the carbon. Dotted partial bonds represent the bonds being formed or broken...
Chemical Shift: Internal References and Solvent Effects01:17

Chemical Shift: Internal References and Solvent Effects

In an NMR sample, precise measurement of the absolute absorption frequencies of nuclei is difficult. A standard internal reference compound is added, and the frequency difference between the reference signal and sample signals is measured.
The internal reference compound generally used in NMR spectroscopy is tetramethylsilane (TMS). TMS is preferred because it is chemically inert, soluble in NMR solvents, and easily removable. Also, the highly shielded methyl protons in TMS yield an intense...
Transition State Theory01:25

Transition State Theory

Transition-state theory, also known as activated-complex theory, provides a molecular-level explanation of reaction rates in both gas-phase and solution-phase reactions. It extends earlier kinetic models by considering the formation of a short-lived, high-energy configuration during a reaction.The progress of a chemical reaction can be represented using a reaction profile, which plots potential energy against the reaction coordinate. As two reactant molecules approach one another, their...

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dmf-g16: A Gaussian Wrapper for Reliable Double-Ended Transition-State Searches With Native Input Formats.

Shin-Ichi Koda1,2, Shinji Saito1,2

  • 1Department of Theoretical and Computational Molecular Science, Institute for Molecular Science, National Institutes of Natural Sciences, Okazaki, Japan.

Journal of Computational Chemistry
|May 4, 2026
PubMed
Summary

dmf-g16 streamlines transition-state (TS) searches in computational chemistry by integrating the Direct MaxFlux (DMF) method with Gaussian software. This approach significantly enhances the reliability of identifying reaction pathways and transition states.

Keywords:
GaussianPyDMFdirect MaxFlux methodflat‐bottom elastic network modeltransition‐state searches

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Area of Science:

  • Computational Chemistry
  • Chemical Reaction Dynamics
  • Quantum Chemistry

Background:

  • Transition-state (TS) searches are crucial for understanding chemical reaction mechanisms.
  • Advanced TS search methods are often difficult to integrate into standard computational workflows.
  • Existing methods like Gaussian's QST2/QST3 require significant user effort and have limitations in reliability.

Purpose of the Study:

  • To introduce dmf-g16, a Gaussian-specific front end for the Direct MaxFlux (DMF) reaction-path optimization method.
  • To enable easier integration of advanced TS search capabilities into routine computational chemistry workflows.
  • To improve the reliability and success rate of transition-state searches.

Main Methods:

  • Developed dmf-g16 as a front end for the PyDMF implementation of the Direct MaxFlux (DMF) method.
  • Integrated dmf-g16 with Gaussian, allowing native QST2/QST3 input files to be used.
  • Employed Gaussian as an external energy calculator for DMF's explicit path optimization, followed by TS refinement.

Main Results:

  • Benchmarks on 121 reactions demonstrated a significant increase in TS search reliability.
  • The success rate improved from 31.4% with Gaussian QST2 to 93.4% using dmf-g16.
  • While path optimization increases computational cost, the overall wall-clock time remains manageable, comparable to a few times that of QST2.

Conclusions:

  • dmf-g16 offers a user-friendly solution for robust transition-state searches within the Gaussian computational chemistry package.
  • The method significantly enhances the success rate of identifying reaction pathways compared to standard Gaussian methods.
  • Workflow integration is minimal, requiring only the replacement of the Gaussian executable with dmf-g16.