Introduction

MD Fragment Analyzer is a state-of-the-art Python tool that streamlines the analysis of molecular dynamics (MD) simulations. By automatically identifying chemically connected fragments across simulation frames, it provides clear insights into the structural evolution of your system. Whether you're studying protein dynamics or material science, this tool offers the perfect blend of efficiency, flexibility, and user-friendly interaction.

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Overview

Fragment analysis is essential for understanding the structural evolution in MD simulations. This tool computes interatomic distances and determines bonds based on a threshold defined as a scaling factor (default: 1.2) times the sum of atomic radii. Connected atoms are then grouped into fragments using an efficient union-find (disjoint-set) algorithm.

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Key Features

• Fragment Detection

  • Computes interatomic distances from MD simulation data.
  • Detects bonds using a customizable scale factor and selected atomic radii.

• Radii Options

  • Choose between Covalent Radii and Van der Waals Radii for bond determination.

• Periodic Boundary Conditions (PBC)

  • Optionally adjusts interatomic distances to account for periodic boundary conditions using simulation cell dimensions.

• Performance Optimizations

  • Accelerated computations with Numba’s just-in-time (JIT) compilation.
  • Multiprocessing support for handling multiple frames.
  • Optional GPU acceleration (in selected versions) for processing large datasets.

• User-Friendly Interface

  • A Tkinter-based graphical user interface (GUI) that simplifies file selection, parameter configuration, and provides real-time status updates.

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Methodology (v1.4)

1. Input Processing
   Reads MD trajectories from XYZ files and extracts lattice parameters (if available) for periodic systems.

2. Distance Calculation
   Computes the Euclidean distance between every pair of atoms. When PBC is enabled, distances are adjusted according to the simulation cell dimensions.

3. Bond Determination
   A bond is determined if the interatomic distance is less than:

    cutoff = scale * (atomic_radius_atom1 + atomic_radius_atom2)
   

4. Fragment Grouping
   Uses a union-find algorithm to cluster bonded atoms into distinct fragments.

5. Output Generation
   Summarizes each fragment by counting constituent elements and formatting the results as a molecular formula.

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Version Features and Changes

• v1.0
• Initial Release: Basic fragment analysis using ASE for XYZ file reading and union-find for fragment grouping.

• Provided both CPU (via Numba JIT) and GPU (via Numba CUDA) acceleration.

• v1.1

• GUI Enhancements: Improved file selection and parameter configuration interface.

• Introduced multiprocessing support for handling multiple frames.

• v1.2

• Radii Options & PBC: Added support for Van der Waals radii and implemented periodic boundary conditions in distance calculations.

• v1.3

• User Interface Upgrade: Enhanced GUI with a menu bar (including “About” and “Methodology”) and a detailed status message area.

• Improved fragment grouping and error handling.

• v1.4

• Streamlined Methodology: Focused on stable CPU-based computations using essential submodules for better performance and accuracy.

• v1.5

• Expanded Support: Integrated a custom Atoms class and XYZ reader to remove the dependency on ASE.

• Enhanced multiprocessing and optional GPU acceleration to better handle large datasets.

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Requirements

• Python 3.x
• NumPy
• ASE (Atomic Simulation Environment) (v1.5 now uses a custom reader)
• Numba
• Tkinter (usually bundled with Python)
• (Optional) CUDA-compatible GPU for accelerated computations

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Usage

• For CPU + GPU version:

python MDFAv1.3.py

• For CPU only version:

python MDFAv1.4.py

• For compiling to binary without using ase library, consider using:

python MDFAv1.5.py

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Tutorial

This section provides a step-by-step guide on using the GUI for MD fragment analysis.

1. Launching the GUI

When you run the application, the main GUI window appears. Here you can load your MD simulation file and adjust analysis settings.

2. Loading Data & Setting Options

Click the "Load File" button to select your XYZ file. Adjust parameters such as: - Number of Cores - GPU Acceleration (if available) - Radii Type (Covalent or Van der Waals) - Periodic Boundary Conditions

3. Running the Analysis

After configuring your settings, click the "Run Counting" button to start the analysis. The status area will update with progress messages.

4. Viewing the Results

Currently, to visualize the final fragment counts, you can use third-party code or visualization software to process the output data. For example, the following plot shows fragment counts per frame:

Note: A built-in quick visualization function is under development.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Citation

If you use MD Fragment Analyzer in your research, please cite it as follows:

bibtex @misc{Lu2025MDFragmentAnalyzer, author = {Lu, Le}, title = {MD Fragment Analyzer}, year = {2025}, publisher = {GitHub}, howpublished = {\url{https://github.com/lulelaboratory/MD_Fragment_Analyzer}}, note = {Version v1.5. Accessed: 2025-03-03}, doi = {10.5281/zenodo.14961945}

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Acknowledgments

Developed by Le Lu (lulelaboratory@gmail.com) in 2025. For further details or support, please contact the author.