# Introduction¶

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 (http://cms.mpi.univie.ac.at/vasp/), ABINIT (http://www.abinit.org/), 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.

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:

• Please report any bugs and issues at pymatgen’s Github Issues page.

• For help with any pymatgen issue, please use the pymatgen Discourse page. Please note that the pymatgen Google group has been deprecated in favor of Discourse.

• matgenb. For example notebooks.

The code is mightier than the pen.

# Offline docs¶

If you would like to have an offline version of the docs for reference, there are two options:

1. Clone the Github repo and the latest html docs are in the “docs” folder.
2. In Dash or Zeal, go to “User Contributed Docsets”, search for pymatgen and install.

# Development News¶

## Py3k-only with effect from 2019.1.1¶

Pymatgen has supported both Python 2.7 as well as Python 3.x from version 3.0. With increasing support by most standard libraries for Py3k, it no longer makes sense to maintain this dual support going forward. Dual support imposes costs in terms of developmental effort, and also forces compromises in code quality and efficiency. Though some legacy clusters may only come with Py2k installed by default, the recommended approach in any case is to create an isolated Py3k environment.

The pymatgen development team will phase out Py2k support over the course of 2018. From v2018.1.1, new features implemented in pymatgen no longer need to support Py2k (i.e., unittests do not need to pass Py2k testing), though existing features will still be Py2k compatible. From v2019.1.1, pymatgen will be Py3k only.

# 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. We have deprecated the pymatgen examples page in favor of this more sustainable approach going forward. 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 CircleCI and Appveyor for continuous integration on the Linux and Windows platforms, respectively, which ensures that every commit passes a comprehensive suite of unittests. The coverage of the unittests can be seen on coveralls.io.
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.

# Change log¶

• Streamlined Site, PeriodicSite, Molecule and Structure code by abandoning immutability for Site and PeriodicSite.
• VaspInput class now supports a run_vasp method, which can be used to code runnable python scripts for running simple calculations (custodian still recommended for more complex calculations.). For example, the following is a kpoint convergence script that can be submitted in a queue
from pymatgen import MPRester
from pymatgen.io.vasp.sets import MPRelaxSet

source $HOME/.bash_profile # Install numpy and other pydata stack packages via conda. conda install --yes numpy scipy pandas conda install --yes --channel matsci pymatgen  # Usage¶ 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, crystallographic 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. ## Quick start¶ Useful aliases for commonly used objects are now provided. Supported objects include Element, Composition, Structure, Molecule, Spin and Orbital. Here are some quick examples of the core capabilities and objects: >>> import pymatgen as mg >>> >>> si = mg.Element("Si") >>> si.atomic_mass 28.0855 >>> print(si.melting_point) 1687.0 K >>> >>> comp = mg.Composition("Fe2O3") >>> comp.weight 159.6882 >>> # Note that Composition conveniently allows strings to be treated just >>> # like an Element object. >>> comp["Fe"] 2.0 >>> comp.get_atomic_fraction("Fe") 0.4 >>> lattice = mg.Lattice.cubic(4.2) >>> structure = mg.Structure(lattice, ["Cs", "Cl"], ... [[0, 0, 0], [0.5, 0.5, 0.5]]) >>> structure.volume 74.088000000000008 >>> 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_spacegroup_symbol() 'Pm-3m' >>> >>> # 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. >>> structure.to(fmt="poscar") >>> structure.to(filename="POSCAR") >>> structure.to(filename="CsCl.cif") >>> >>> # 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("methane.xyz") >>> mol.to("methane.gjf") >>> >>> # 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] >>> >>> # If you set up your .pmgrc.yaml with your Materials Project API key >>> # You can now easily grab structures from the Materials Project. >>> lifepo4 = mg.get_structure_from_mp("LiFePO4")  The above illustrates only the most basic capabilities of pymatgen. Users are strongly encouraged to explore the usage pages (toc given below). ## API documentation¶ For detailed documentation of all modules and classes, please refer to the API docs. ## More resources¶ 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. ## pmg - Command line tool¶ 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.
3. The pymatgen-diffusion by the Materials Virtual Lab provides additional useful analyses for diffusion in materials.

# Contributing¶

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.

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.

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. The terms of the license are as follows:

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