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An effective mass package

Project description


PyPI version Documentation Status Build Status Test Coverage DOI License: MIT JOSS status

effmass is a Python 3 package for calculating various definitions of effective mass from the electronic bandstructure of a semiconducting material. It consists of a core class that calculates the effective mass and other associated properties of selected bandstructure segments. The module also contains functions for locating bandstructure extrema and plotting approximations to the dispersion.

Examples are provided in a Jupyter notebook here. API documentation is here. Source code is available as a git repository at

The paper directory contains the Vasp input data (POSCAR), Vasp output data (OUTCAR/PROCAR) and band structures generated for an academic paper using this software: Impact of nonparabolic electronic band structure on the optical and transport properties of photovoltaic materials
Phys. Rev. B 99 (8), 085207 - also avaiable on arXiv..


effmass can:

Read in a bandstructure: This requires the VASP output files PROCAR and OUTCAR. It is assumed you have walked through a 1D slice of the Brillouin Zone, capturing the maxima and minima of interest. effmass uses the Python package vasppy for parsing VASP output.

Locate extrema: These correspond to the valence band maxima and conduction band minima. Maxima and minima within a certain energy range can also be located.

Calculate curvature, transport and optical effective masses: The curvature (aka inertial) and transport masses are calculated using the derivatives of a fitted polynomial function. The optical effective mass can also be calculated assuming a Kane dispersion.

Assess the extent of non-parabolicity: Parameters of the Kane quasi-linear dispersion are calculated to quantify the extent of non-parabolicity over a given energy range.

Calculate the quasi-fermi level for a given carrier concentration: This requires the VASP output file DOSCAR. Using density-of-states data and assuming no thermal smearing, effmass can calculate the energy to which states are occupied. This is a useful approximation to the quasi-Fermi level.

Plot fits to the dispersion: Selected bandstructure segments and approximations to the dispersion (assuming a Kane, quadratic, or higher order fit) can be visualised.

The effmass package is aimed towards theoretical solid state physicists and chemists who have a basic familiarity with Python. Depending on the functionality and level of approximation you are looking for, it may be that one of the packages listed here will suit your needs better.


Please use the Github issue tracker for feature requests and bug reports.

If you would like to contribute please do so via a pull request. All contributors must read and respect the code of conduct. In particular, we welcome contributions which would extend effmass so that it is able to parse output from other electronic structure codes.


effmass is a Python 3 package and requires key packages from the SciPy ecosystem: SciPy, NumPy and Matplotlib. If you have not installed these packages before, it may be best to install them using your preferred package manager (eg: Homebrew). Note that together they will use >100MB of disk space. effmass can then be built using the Python package manager pip:

pip3 install --user effmass

Or download the latest release from GitHub, and install

cd effmass
python3 install

Or clone the latest development version

git clone

and install the same way.

cd effmass
python3 install 


Automated testing of the latest commit happens here.

Manual tests can be run using

python3 -m pytest

This code has been tested with Python versions 3.6.


An overview of the features of effmass along with example code is contained in a Jupyter notebook, which is available in the paper directory.

API documentation is available here.

Citing effmass

If you use this code in your research, please cite the following paper:

Whalley, Lucy D. (2018). effmass - an effective mass package. The Journal of Open Source Software, 3(28) 797.


  author       = {Lucy D. Whalley},
  title        = {effmass: An effective mass package},
  volume       = {3},
  issue        = {28},
  pages        = {797},
  month        = {Aug},
  year         = {2018},
  doi          = {10.21105/joss.00797},
  url          = {}

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