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Pymatgen (Python Materials Genomics) is a robust, open-source Python library for materials analysis. These are some of the main features:

  1. Highly flexible classes for the representation of Element, Site, Molecule, Structure objects.

  2. Extensive input/output support, including support for VASP (, ABINIT (, CIF, Gaussian, XYZ, and many other file formats.

  3. Powerful analysis tools, including generation of phase diagrams, Pourbaix diagrams, diffusion analyses, reactions, etc.

  4. Electronic structure analyses, such as density of states and band structure.

  5. Integration with the Materials Project REST API, Crystallography Open Database and other external data sources.

As of 2022, pymatgen only supports Python 3.8 and above. Our support schedule follows closely that of the Scientific Python software stack, i.e., when packages such as numpy drops support for Python versions, we will drop support for newer versions. Similarly, support for new Python versions will be adopted only when most of the core dependencies support the new Python versions.

Pymatgen is free to use. However, we also welcome your help to improve this library by making your own contributions. These contributions can be in the form of additional tools or modules you develop, or feature requests and bug reports. The following are resources for pymatgen:

  • Official documentation

  • Offline docs: HTML are in the pymatgen Github repo’s docs folder. Dash or Zeal docs can be searched and downloaded from “User Contributed Docsets”.

  • Bug reports or feature requests: Please submit a GitHub Issue.

  • Code contributions via pull requests welcome.

  • For help with usage that are unrelated to bugs or feature requests, please use the pymatgen Discourse page.

  • matgenb provides some Jupyter notebooks demonstrating functionality.

  • Follow us on Twitter to get news and tips.

    The code is mightier than the pen.

Major Announcement (v2022.0.*)

A backwards incompatible change has been introduced in v2022.0.*. Pymatgen root-level convenience imports have been removed from in preparation for a change to a more modular, extensible namespace package architecture that will allow more developers to contribute. If your existing code uses from pymatgen import <something>, you will need to make modifications. MPRester should now be imported from pymatgen.ext.matproj. All other convenience objects such as Element, Species, Lattice, Structure, etc. should be imported from pymatgen.core. There are a few simple ways you can respond to this change:

  • To migrate your code to be compatible with v2022.0.* (it will still be compatible with pymatgen<2022.0.0 since all the imports were already available in previous versions), you need to replace all instances of from pymatgen import MPRester with from pymatgen.ext.matproj import MPRester, followed by replacing all instances of from pymatgen import with from pymatgen.core import. These two steps have to be done in that sequence, since MPRester and the other core imports exist in different subpackages. The easiest way is to use an IDE such as Pycharm to run a Replace in Files on the root directory of your code.

  • The pymatgen maintainers have also come up with the following terminal commands you can use to perform the migration. On a Mac:

    find . -name '*.py' | xargs sed -i "" 's/from pymatgen import MPRester/from pymatgen.ext.matproj import MPRester/g'
    find . -name '*.py' | xargs sed -i "" 's/from pymatgen import/from pymatgen.core import/g'

    On Linux:

    find . -name '*.py' | xargs sed -i 's/from pymatgen import MPRester/from pymatgen.ext.matproj import MPRester/g'
    find . -name '*.py' | xargs sed -i 's/from pymatgen import/from pymatgen.core import/g'

    This should resolve most import errors, though you may have to fix a few issues manually, e.g., if your code contains something like from pymatgen import Element, MPRester, which will now need to be split into two lines.

Last but not least, one option is to pin to pymatgen==2021.*.*, which is the last version to contain the root-level convenience imports, if you are not planning to use future new pymatgen functionality. The new breaking change will become default from year 2022. Backports to 2021.*.* will still occur for critical bug fixes.

Matgenie & Examples

The Materials Virtual Lab has developed a matgenie web app which demonstrates some of the basic functionality of pymatgen, as well as a matgenb repository of Jupyter notebooks for common and advanced use cases. One of the ways you can contribute is to fork the matgenb repo and add your own examples.

Below are a quick look at some of the graphical output possible.


Top: (left) Phase and (right) Pourbaix diagram from the Materials API. Bottom left: Calculated bandstructure plot using pymatgen’s parsing and plotting utilities. Bottom right: Arrhenius plot using pymatgen’s DiffusionAnalyzer.

Why use pymatgen?

There are many materials analysis codes out there, both commercial and free. So you might ask - why should I use pymatgen over others? Pymatgen offer several advantages over other codes out there:

  1. It is (fairly) robust. Pymatgen is used by thousands of researchers, and is the analysis code powering the Materials Project. The analysis it produces survives rigorous scrutiny every single day. Bugs tend to be found and corrected quickly. Pymatgen also uses Github Actions for continuous integration, which ensures that every new code passes a comprehensive suite of unittests.

  2. It is well documented. A fairly comprehensive documentation has been written to help you get to grips with it quickly.

  3. It is open. You are free to use and contribute to pymatgen. It also means that pymatgen is continuously being improved. We will attribute any code you contribute to any publication you specify. Contributing to pymatgen means your research becomes more visible, which translates to greater impact.

  4. It is fast. Many of the core numerical methods in pymatgen have been optimized by vectorizing in numpy/scipy. This means that coordinate manipulations are extremely fast and are in fact comparable to codes written in other languages. Pymatgen also comes with a complete system for handling periodic boundary conditions.

  5. It will be around. Pymatgen is not a pet research project. It is used in the well-established Materials Project. It is also actively being developed and maintained by the Materials Virtual Lab, the ABINIT group and many other research groups.

  6. A growing ecosystem of developers and add-ons. Pymatgen has contributions from materials scientists all over the world. We also now have an architecture to support add-ons that expand pymatgen’s functionality even further. Check out the contributing page and add-ons page for details and examples.

Please review the coding guidelines.

Change log


  • Fix mem leak in pbc_shortest_vector cython code. (@stichri)

  • Set all cython code to language level 3.

Older versions

Getting pymatgen

If you are absolutely new to Python and/or are using Windows, the easiest installation process is using conda. If you already have conda installed, pymatgen can be installed from the conda-forge channel using the following command:

conda install --channel conda-forge pymatgen

Note that you might need to ensure a relatively recent version of gcc is available to compile pymatgen. You can use conda to get that:

conda install gcc

Pymatgen is under active development, and new features are added regularly. To upgrade pymatgen to the latest version, use the following command:

conda upgrade pymatgen

Step-by-step instructions for all platforms are available at the installation page.

The version at the Python Package Index (PyPI) is always the latest stable release that is relatively bug-free. The easiest way to install pymatgen on any system is to use pip:

pip install pymatgen

Wheels for Mac and Windows have been built for convenience. Similarly, you might need to ensure you have a relatively recent version of gcc.

To upgrade pymatgen via pip:

pip install --upgrade pymatgen

The bleeding edge developmental version is at the pymatgen Github repo. The developmental version is likely to be more buggy, but may contain new features. The Github version include complete test files. After cloning the source, you can type in the root of the repo:

pip install .

or to install the package in developmental mode:

pip install -e .

Detailed installation instructions, including installation of option dependencies, set up for POTCAR generation, Materials Project REST interface usage, setup for developers, etc.are given on this page.

For some extras, you can also install the optional dependencies using:

pip install pymatgen[extra]

For an always up-to-date list of extras, consult the’s extras_require.

If you are installing pymatgen on shared computing clusters, e.g., the XSEDE or NERSC resources in the US, the best way is to use conda to perform a local install. This guarantees the right version of python and all dependencies:

bash -b

# Reload bash profile.
source $HOME/.bashrc
source $HOME/.bash_profile

# Install numpy and other pydata stack packages via conda.
conda install --yes numpy scipy pandas
conda install --yes --channel conda-forge pymatgen


pymatgen overview

Overview of a typical workflow for pymatgen.

The figure above provides an overview of the functionality in pymatgen. A typical workflow would involve a user converting data (structure, calculations, etc.) from various sources (first principles calculations, crystal and molecule input files, Materials Project, etc.) into Python objects using pymatgen’s io packages, which are then used to perform further structure manipulation or analyses.

Here are some quick examples of the core capabilities and objects:

>>> import pymatgen.core as mg
>>> si = mg.Element("Si")
>>> si.atomic_mass
>>> print(si.melting_point)
1687.0 K
>>> comp = mg.Composition("Fe2O3")
>>> comp.weight
>>> # Note that Composition conveniently allows strings to be treated just
>>> # like an Element object.
>>> comp["Fe"]
>>> comp.get_atomic_fraction("Fe")
>>> lattice = mg.Lattice.cubic(4.2)
>>> structure = mg.Structure(lattice, ["Cs", "Cl"],
...                          [[0, 0, 0], [0.5, 0.5, 0.5]])
>>> structure.volume
>>> structure[0]
PeriodicSite: Cs (0.0000, 0.0000, 0.0000) [0.0000, 0.0000, 0.0000]
>>> # You can create a Structure using spacegroup symmetry as well.
>>> li2o = mg.Structure.from_spacegroup("Fm-3m", mg.Lattice.cubic(3),
                                        ["Li", "O"],
                                        [[0.25, 0.25, 0.25], [0, 0, 0]])
>>> # Integrated symmetry analysis tools from spglib.
>>> from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
>>> finder = SpacegroupAnalyzer(structure)
>>> finder.get_space_group_symbol()
>>> # Convenient IO to various formats. You can specify various formats.
>>> # Without a filename, a string is returned. Otherwise,
>>> # the output is written to the file. If only the filenmae is provided,
>>> # the format is intelligently determined from a file.
>>> # Reading a structure is similarly easy.
>>> structure = mg.Structure.from_str(open("CsCl.cif").read(), fmt="cif")
>>> structure = mg.Structure.from_file("CsCl.cif")
>>> # Reading and writing a molecule from a file. Supports XYZ and
>>> # Gaussian input and output by default. Support for many other
>>> # formats via the optional openbabel dependency (if installed).
>>> methane = mg.Molecule.from_file("")
>>> # Pythonic API for editing Structures and Molecules (v2.9.1 onwards)
>>> # Changing the specie of a site.
>>> structure[1] = "F"
>>> print(structure)
Structure Summary (Cs1 F1)
Reduced Formula: CsF
abc   :   4.200000   4.200000   4.200000
angles:  90.000000  90.000000  90.000000
Sites (2)
1 Cs     0.000000     0.000000     0.000000
2 F     0.500000     0.500000     0.500000
>>> # Changes species and coordinates (fractional assumed for structures)
>>> structure[1] = "Cl", [0.51, 0.51, 0.51]
>>> print(structure)
Structure Summary (Cs1 Cl1)
Reduced Formula: CsCl
abc   :   4.200000   4.200000   4.200000
angles:  90.000000  90.000000  90.000000
Sites (2)
1 Cs     0.000000     0.000000     0.000000
2 Cl     0.510000     0.510000     0.510000
>>> # Replaces all Cs in the structure with K
>>> structure["Cs"] = "K"
>>> print(structure)
Structure Summary (K1 Cl1)
Reduced Formula: KCl
abc   :   4.200000   4.200000   4.200000
angles:  90.000000  90.000000  90.000000
Sites (2)
1 K     0.000000     0.000000     0.000000
2 Cl     0.510000     0.510000     0.510000
>>> # Replaces all K in the structure with K: 0.5, Na: 0.5, i.e.,
>>> # a disordered structure is created.
>>> structure["K"] = "K0.5Na0.5"
>>> print(structure)
Full Formula (K0.5 Na0.5 Cl1)
Reduced Formula: K0.5Na0.5Cl1
abc   :   4.209000   4.209000   4.209000
angles:  90.000000  90.000000  90.000000
Sites (2)
  #  SP                   a    b    c
---  -----------------  ---  ---  ---
  0  K:0.500, Na:0.500  0    0    0
  1  Cl                 0.5  0.5  0.5
>>> # Because structure is like a list, it supports most list-like methods
>>> # such as sort, reverse, etc.
>>> structure.reverse()
>>> print(structure)
Structure Summary (Cs1 Cl1)
Reduced Formula: CsCl
abc   :   4.200000   4.200000   4.200000
angles:  90.000000  90.000000  90.000000
Sites (2)
1 Cl     0.510000     0.510000     0.510000
2 Cs     0.000000     0.000000     0.000000
>>> # Molecules function similarly, but with Site and Cartesian coords.
>>> # The following changes the C in CH4 to an N and displaces it by 0.01A
>>> # in the x-direction.
>>> methane[0] = "N", [0.01, 0, 0]

The above illustrates only the most basic capabilities of pymatgen. Users are strongly encouraged to explore the usage pages (toc given below).

For detailed documentation of all modules and classes, please refer to the API docs.

The founder and maintainer of pymatgen, Shyue Ping Ong, has conducted several workshops (together with Anubhav Jain) on how to effectively use pymatgen (as well as the extremely useful custodian error management and FireWorks workflow software. The slides for these workshops are available on the Materials Virtual Lab.

To demonstrate the capabilities of pymatgen and to make it easy for users to quickly use the functionality, pymatgen comes with a set of useful scripts that utilize the library to perform all kinds of analyses. These are installed to your path by default when you install pymatgen through the typical installation routes.

Here, we will discuss the most versatile of these scripts, known as pmg. The typical usage of pmg is:

pmg {setup, config, analyze, plotdos, plotchgint, convert, symm, view, compare} additional_arguments

At any time, you can use "pmg --help" or "pmg subcommand --help" to bring up a useful help message on how to use these subcommands. With effect from v4.6.0, pmg also supports bash completion using argcomplete, which is useful given the many options available in the cli tool. To enable argcomplete, pip install argcomplete and either follow argcomplete’s instructions for enabling global completion, or add the following line to your .bash_profile (this method usually works more reliably):

eval "$(register-python-argcomplete pmg)"

Here are a few examples of typical usages:

# Parses all vasp runs in a directory and display the basic energy
# information. Saves the data in a file called vasp_data.gz for subsequent
# reuse.

pmg analyze .

# Plot the dos from the vasprun.xml file.

pmg plot --dos vasprun.xml

# Convert between file formats. The script attempts to intelligently
# determine the file type. Input file types supported include CIF,
# vasprun.xml, POSCAR, CSSR. You can force the script to assume certain file
# types by specifying additional arguments. See pmg convert -h.

pmg structure --convert --filenames input_filename output_filename.

# Obtain spacegroup information using a tolerance of 0.1 angstroms.

pmg structure --symmetry 0.1 --filenames filename1 filename2

# Visualize a structure. Requires VTK to be installed.

pmg view filename

# Compare two structures for similarity

pmg structure --group element --filenames filename1 filename2

# Generate a POTCAR with symbols Li_sv O and the PBE functional

pmg potcar --symbols Li_sv O --functional PBE

Some add-ons are available for pymatgen today:

  1. The pymatgen-db add-on provides tools to create databases of calculated run data using pymatgen.

  2. The custodian package provides a JIT job management and error correction for calculations and is used by the Materials Project for high-throughput calculations.

  3. pymatgen-analysis-diffusion by the Materials Virtual Lab provides modules for diffusion analysis, including path determination for NEB calculations, analysis of MD trajectories (RDF, van Hove, Arrhenius plots, etc.)

A comprehensive listing is provided at the addons page.


Pymatgen is developed by a team of volunteers. It is started by a team comprising of MIT and Lawrence Berkeley National Laboratory staff to be a robust toolkit for materials researchers to perform advanced manipulations of structures and analyses.

For pymatgen to continue to grow in functionality and robustness, we rely on other volunteers to develop new analyses and report and fix bugs. We welcome anyone to use our code as-is, but if you could take a few moment to give back to pymatgen in some small way, it would be greatly appreciated. A benefit of contributing is that your code will now be used by other researchers who use pymatgen, and we will include an acknowledgement to you (and any related publications) in pymatgen.

Reporting bugs

A simple way that anyone can contribute is simply to report bugs and issues to the developing team. Please report any bugs and issues at pymatgen’s Github Issues page. For help with any pymatgen issue, consult Stack Overflow and if you cannot find an answer, please post a question with the tag pymatgen.

Developing new functionality

Another way to contribute is to submit new code/bugfixes to pymatgen. The best way for anyone to develop pymatgen is by adopting the collaborative Github workflow (see contributing page).

How to cite pymatgen

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

Shyue Ping Ong, William Davidson Richards, Anubhav Jain, Geoffroy Hautier, Michael Kocher, Shreyas Cholia, Dan Gunter, Vincent Chevrier, Kristin A. Persson, Gerbrand Ceder. Python Materials Genomics (pymatgen) : A Robust, Open-Source Python Library for Materials Analysis. Computational Materials Science, 2013, 68, 314–319. doi:10.1016/j.commatsci.2012.10.028

In addition, some of pymatgen’s functionality is based on scientific advances / principles developed by various scientists. Please refer to the references page for citation info.


Pymatgen is released under the MIT License.

About the Team

Shyue Ping Ong of the Materials Virtual Lab started Pymatgen in 2011, and is still the project lead.

The Pymatgen Development Team is the set of all contributors to the pymatgen project, including all subprojects.

The full list of contributors are listed in the team page.

Indices and tables