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Updated: Jan 24, 2026

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

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Published on: January 31, 2020

Visualizing the energy landscape for a molecular dynamics trajectory.

Vilmos Neuman^1,2, Patryk A Wesołowski², Krzysztof K Bojarski^3,4

¹Department of Chemistry, Physical and Theoretical Chemistry Laboratory, University of Oxford, South Parks Road, Oxford OX1 3QZ, United Kingdom.

The Journal of Chemical Physics

|January 23, 2026

Summary

This summary is machine-generated.

We developed an open-source program to visualize molecular dynamics energy landscapes using disconnectivity graphs. This tool efficiently identifies conformational pathways and hierarchies directly from simulation data without geometry optimization.

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Area of Science:

Computational Chemistry
Biophysics
Materials Science

Background:

Molecular dynamics (MD) simulations generate vast amounts of data.
Visualizing the complex energy landscape explored by MD is challenging.
Interpreting conformational changes and pathways requires efficient analytical tools.

Purpose of the Study:

To introduce an open-source program for converting MD trajectories into disconnectivity graphs.
To provide a concise and interpretable visualization of the energy landscape.
To enable direct analysis of explored conformational space without geometry optimization.

Main Methods:

Applies Savitzky-Golay smoothing to per-frame thermodynamic traces (potential energy or enthalpy).
Identifies local extrema as proxies for minima and transition states.
Generates files for disconnectivity graph construction, compatible with various ensembles and simulation types.

Main Results:

Processes large MD datasets (10^4-10^5 frames) rapidly on standard hardware.
Produces approximate representations of energy landscape topology.
Captures visited structures and pathways at selected resolutions, reflecting the MD timescale.

Conclusions:

The developed program offers an efficient method for analyzing MD simulation data.
Disconnectivity graphs provide interpretable structural summaries of conformational hierarchies.
The approach facilitates understanding of molecular behavior and landscape organization directly from simulations.