The iprPy project is a collection of tools and resources for developing and performing classical atomistic simulations. It was originally created to support basic property calculations for the NIST Interatomic Potential Repository allowing for the different hosted potentials to be evaluated and compared. The robustness of iprPy is such that it can also be used for handling more complex materials analyses for research purposes.

The design principles for iprPy are:

• Full documentation is provided for all calculations explaining not only how to perform the calculations, but also detailing the strengths and weaknesses of the underlying routines.
• Each calculation is a complete, independent unit of work with clear input parameters and structured results. This makes it possible to easily share the calculations and provide results that are both human- and machine-readable.
• Calculations should be easily adapted to work with high-throughput resources for performing over a wide range of potentials and conditions.

1. Install Python 2.7

   1. Using an Anaconda Python distribution is preferred as the scipy family of tools is heavily used.
   2. The package 'xmltodict'_ needs to be manually installed beforehand. This can easily be done with the terminal command:

   pip install xmltodict

   3. All other requirements should come with Anaconda or will automatically install during step 4 (let me know if not true).

2. All of the files for iprPy can be found at:

   • https://github.com/usnistgov/iprPy for the stable release.
   • https://github.com/lmhale99/iprPy for the development version(s).

3. Download or clone the whole project to a local directory.

4. In a terminal, go into the iprPy root directory and add the iprPy package to Python with the command:

   python setup.py develop

The iprPy guide document provides an overview of the different parts of the iprPy framework. This document is meant to introduce new users to the designing principles used by the framework.

More detailed documentation related to specific functions and components of the framework can be found in the form of Jupyter Notebooks in the docs directory.