CALPHAD tools for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria.
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README.rst

pycalphad, a library for the CALculation of PHAse Diagrams

Join the chat at https://gitter.im/pycalphad/pycalphad Test Coverage Windows Build Status Build Status Development Status Latest version Supported Python versions License

Note: Unsolicited pull requests are _happily_ accepted!

pycalphad is a free and open-source Python library for designing thermodynamic models, calculating phase diagrams and investigating phase equilibria within the CALPHAD method. It provides routines for reading Thermo-Calc TDB files and for solving the multi-component, multi-phase Gibbs energy minimization problem.

The purpose of this project is to provide any interested people the ability to tinker with and improve the nuts and bolts of CALPHAD modeling without having to be a computer scientist or expert programmer.

For assistance in setting up your Python environment and/or collaboration opportunities, please contact the author by e-mail or using the issue tracker on GitHub.

pycalphad is licensed under the MIT License. See LICENSE.txt for details.

Required Dependencies:

  • Python 2.7+ or 3.5+
  • matplotlib, numpy, scipy, sympy, xarray, pyparsing, tinydb, dask, cyipopt

Installation

See Installation Instructions.

Examples

Jupyter notebooks with examples are available on NBViewer and pycalphad.org.

Documentation

See the documentation on pycalphad.org.

Getting Help

Questions about installing and using pycalphad can be addressed in the pycalphad Google Group. Technical issues and bugs should be reported on on GitHub. A public chat channel is available on Gitter.

Citing

If you use pycalphad in your research, please consider citing the following work:

Otis, R. & Liu, Z.-K., (2017). pycalphad: CALPHAD-based Computational Thermodynamics in Python. Journal of Open Research Software. 5(1), p.1. DOI: http://doi.org/10.5334/jors.140

Acknowledgements

Development has been made possible in part through NASA Space Technology Research Fellowship (NSTRF) grant NNX14AL43H, and is supervised by Prof. Zi-Kui Liu in the Department of Materials Science and Engineering at the Pennsylvania State University. We would also like to acknowledge technical assistance on array computations from Denis Lisov.