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Molecular Dynamics analysis

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This is the documentation for the open-source Python project - polypy, A library designed to facilitate the analysis of DL_POLY and DL_MONTE calculations. polypy is built on existing Python packages that those in the solid state physics/chemistry community should already be familiar with. It is hoped that this tool will bring some benfits to the solid state community and facilitate data analysis and the generation of publication ready plots (powered by Matplotlib.)

The main features include:

  1. Method to analyse the number denisty of a given species in one and two dimensions.

    • Generate a plot of the total number of species in bins perpendicular to a specified direction.
    • Generate a plot of the total number of species in cuboids parallel to a specified direction.
  2. Method calculate the charge density from the number density.

    • Convert number densities of all species in bins perpendicular to a specified direction into the charge density.
  3. Calculate the electric field and electrostatic potential from the charge density.

    • Solves the Poisson Boltzmann equation to convert the charge density into the electric field and the electrostatic potential.
  4. Calculate the diffusion coefficient for a given species from a mean squared displacement.

    • Carries out a mean squared displacement on an MD trajectory.
    • Calculates the diffusion coefficient.
    • Uses the density analysis and the diffusion coefficient to calculate the ionic conductivity.

<figcaption>(a) Mean squared displacement for calcuim fluoride. (b) System volume of calcium fluoride during a molecular dynamics simulation. (c) Cerium and oxygen number density at a grain boundary. (d) Electrostatic potential across a grain boundary of cerium oxide. (e) Center of mass of cerium (blue) and oxygen (orange) in a cerium oxide grain boundary in two dimensions.</figcaption>

The code has been developed to analyse DL_POLY and DL_MONTE calculations however other codes can be incorporated if there is user demand. Other formats, such as pdb or xyz can be converted to DL_POLY format with codes such as atomsk and then analysed with polypy. Users are welcome to increase the file coverage by adding a reading function for a different format. This can be accomplished by adding to the read module which has a class for each unique file type that converts it to a object.

polypy was developed during a PhD project and as such the functionality focuses on the research questions encountered during that project, which we should clarify are wide ranging. Code contributions aimed at expanding the code to new of problems are encouraged.

polypy is free to use.


A full list of examples can be found in the examples folder of the git repository, these include both the Python scripts and jupyter notebook tutorials which combine the full theory with code examples. It should be noted however that DL_POLY HISTORY files and DL_MONTE ARCHIVE files are sizable (1-5GB) and as such only short example trajectories are included in this repository. Notebooks are provided here to illustrate the theory but are not practicle.


polypy is a Python 3 package and requires a typical scientific Python stack. Use of the tutorials requires Anaconda/Jupyter to be installed.

To build from source:

pip install -r requirements.txt

python build

python install

Or alternatively install with pip

pip install polypy

Using conda,

conda skeleton pypi polypy

conda build polypy

conda install --use-local polypy


Tests can be run by typing:

python test

in the root directory.


To build the documentation from scratch

cd docs
make html

An online version of the documentation can be found here. The documentation contains an extensive explanation of the underlying theory, function documentation and tutorials.


polypy is made available under the MIT License.

Detailed requirements

polypy is compatible with Python 3.5+ and relies on a number of open source Python packages, specifically:

  • matplotlib
  • numpy
  • scipy
  • coveralls
  • coverage
  • seaborn
  • pandas
  • jupyter
  • nbsphinx
  • jupyter-sphinx==0.2.4
  • sphinx_rtd_theme



If you have questions regarding any aspect of the software then please get in touch with the developer Adam Symington via email - Alternatively you can create an issue on the Issue Tracker.


There may be bugs. If you think you've caught one, please report it on the Issue Tracker. This is also the place to propose new ideas for features or ask questions about the design of polypy. Poor documentation is considered a bug so feel free to request improvements.

Code contributions

We welcome help in improving and extending the package. This is managed through Github pull requests; for external contributions we prefer the "fork and pull" workflow while core developers use branches in the main repository:

  1. First open an Issue to discuss the proposed contribution. This discussion might include how the changes fit polypy's scope and a general technical approach.
  2. Make your own project fork and implement the changes there. Please keep your code style compliant with PEP8.
  3. Open a pull request to merge the changes into the main project. A more detailed discussion can take place there before the changes are accepted.

For further information please contact Adam Symington,


Listed below are a series of useful additions that we would like to make to the codebase. Users are encouraged to fork the repository and work on any of these problems. Indeed, if functionality is not listed below you are more than welcome to add it.

  • RDF
  • Diagonal slices
  • Regional MSDs in a cube


  • Adam R.Symington



This package was written during a PhD project that was funded by AWE and EPSRC (EP/R010366/1). The polypy software package was developed to analyse data generated using the Balena HPC facility at the University of Bath and the ARCHER UK National Supercomputing Service ( via our membership of the UK's HEC Ma-terials Chemistry Consortium funded by EPSRC (EP/L000202).The author would like to thank Andrew R. McCluskey, Benjamin Morgan, Marco Molinari, James Grant and Stephen C. Parker for their help and guidance during this PhD project.

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