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Pymatgen (Python Materials Genomics) is a robust, open-source Python libraryfor materials analysis. These are some of the main features:
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- Highly flexible classes for the representation of Element, Site, Molecule,Structure objects.
- 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.
- Powerful analysis tools, including generation of phase diagrams, Pourbaixdiagrams, diffusion analyses, reactions, etc.
- Electronic structure analyses, such as density of states and band structure.
- Integration with the Materials Project REST API, Crystallography OpenDatabase and other external data sources.
As of 2020, pymatgen only supports Python 3 and above.
Pymatgen is free to use. However, we also welcome your help to improve thislibrary by making your own contributions. These contributions can be in theform of additional tools or modules you develop, or feature requests and bugreports. The following are resources for pymatgen:
- Please report any bugs and issues at pymatgen’s Github Issuespage.
- For help with any pymatgen issue, please use the pymatgen Discourse page. Please note that the pymatgen Googlegroup has been deprecated in favor of Discourse.
- Twitter. Follow to get news and tips.
- matgenb. For example notebooks.
Offline docs¶
If you would like to have an offline version of the docs for reference, thereare two options:
- Clone the Github repo and the latest html docs are in the “docs” folder.
- In Dash or Zeal, go to“User Contributed Docsets”, search for pymatgen and install.
Matgenie & Examples¶
The Materials Virtual Lab has developed amatgenie web app whichdemonstrates some of the basic functionality of pymatgen, as well as amatgenb repository ofJupyter notebooks for common and advanced use cases. We have deprecated thepymatgen 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 ownexamples.
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 andplotting utilities. Bottom right: Arrhenius plot using pymatgen’sDiffusionAnalyzer.¶
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 offerseveral advantages over other codes out there:
- It is (fairly) robust. Pymatgen is used by thousands of researchers,and is the analysis code powering the Materials Project. The analysis itproduces survives rigorous scrutiny every single day. Bugs tend to befound and corrected quickly. Pymatgen also usesCircleCI and Appveyorfor continuous integration on the Linux and Windows platforms,respectively, which ensures that every commit passes a comprehensive suiteof unittests. The coverage of the unittests can be seen oncoveralls.io.
- It is well documented. A fairly comprehensive documentation has beenwritten to help you get to grips with it quickly.
- It is open. You are free to use and contribute to pymatgen. It also meansthat pymatgen is continuously being improved. We will attribute any code youcontribute to any publication you specify. Contributing to pymatgen meansyour research becomes more visible, which translates to greater impact.
- It is fast. Many of the core numerical methods in pymatgen have beenoptimized by vectorizing in numpy/scipy. This means that coordinatemanipulations are extremely fast and are in fact comparable to codeswritten in other languages. Pymatgen also comes with a complete system forhandling periodic boundary conditions.
- It will be around. Pymatgen is not a pet research project. It is used inthe well-established Materials Project. It is also actively being developedand maintained by the Materials Virtual Lab, the ABINIT group and manyother research groups.
Please review the coding guidelines.
Change log¶
v2020.9.14¶
- Sony cyber shot download to mac. New Plotly backend for PhaseDiagram plotting (@mattmcdermott, #1936)
- New reporting and logging of Materials Project database version in MPRester (@mkhorton, #1945)
- Improvements and bug fixes with mcsqs integration (@rwoodsrobinson, #1942)
- Improvements to PackmolRunner (@rkingsbury, #1947)
- Improvements to ComputerEntry (@rkingsbury, #1948)
- Improvements for MPScanSet (@rkingsbury, #1940)
- Bug fix for Surface and Composition (@gpetretto, #1937)
- Bug fix for EwaldSummation serialization (@lbluque, #1932)
- Bug fix for SeeK k-path (@Ian496, #1930)
- Fix for deprecation warning in MPRester (@rkingsbury, #1951)
Getting pymatgen¶
If you are absolutely new to Python and/or are using Windows, the easiestinstallation process is using conda. If youalready have conda installed, pymatgen can be installed from the conda-forgechannel using the following command:
Note that you might need to ensure a relatively recent version of gcc isavailable to compile pymatgen. You can use conda to get that:
Pymatgen is under active development, and new features are added regularly. Toupgrade pymatgen to the latest version, use the following command:
Step-by-step instructions for all platforms are available at theinstallation page.
The version at the Python Package Index (PyPI) is always the latest stablerelease that is relatively bug-free. The easiest way to install pymatgen onany system is to use pip:
Wheels for Mac and Windows have been built for convenience. Similarly, you mightneed to ensure you have a relatively recent version of gcc.
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To upgrade pymatgen via pip:
The bleeding edge developmental version is at the pymatgen Github repo. The developmentalversion is likely to be more buggy, but may contain new features. TheGithub version include complete test files. Aftercloning the source, you can type in the root of the repo:
or to install the package in developmental mode:
Detailed installation instructions, including installation ofoption dependencies, set up for POTCAR generation, Materials Project RESTinterface usage, setup for developers, etc.are given on thispage.
For some extras, you can also install the optional dependencies using:
For an always up-to-date list of extras, consult the setup.py’s extras_require.
If you are installing pymatgen on shared computing clusters, e.g., the XSEDEor NERSC resources in the US, the best way is to use conda to perform a localinstall. This guarantees the right version of python and all dependencies:
Usage¶
The figure above provides an overview of the functionality in pymatgen. Atypical workflow would involve a user converting data (structure, calculations,etc.) from various sources (first principles calculations, crystallographic andmolecule input files, Materials Project, etc.) into Python objects usingpymatgen’s io packages, which are then used to perform further structuremanipulation or analyses.
Useful aliases for commonly used objects are now provided. Supported objectsinclude Element, Composition, Structure, Molecule, Spin and Orbital. Here aresome quick examples of the core capabilities and objects:
The above illustrates only the most basic capabilities of pymatgen. Users arestrongly encouraged to explore the usage pages (toc given below).
For detailed documentation of all modules and classes, please refer to theAPI docs.
The founder and maintainer of pymatgen, Shyue Ping Ong, has conducted severalworkshops (together with Anubhav Jain) on how to effectively use pymatgen (aswell as the extremely useful custodian error management and FireWorksworkflow software. The slides for these workshops are available on theMaterials Virtual Lab.
To demonstrate the capabilities of pymatgen and to make it easy for users toquickly use the functionality, pymatgen comes with a set of useful scriptsthat utilize the library to perform all kinds of analyses. These areinstalled to your path by default when you install pymatgen through thetypical installation routes.
Here, we will discuss the most versatile of these scripts, known aspmg. The typical usage of pmg is:
At any time, you can use
'pmg--help'
or 'pmgsubcommand--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 usingargcomplete, which is useful given the many options available in the cli tool.To enable argcomplete, pipinstallargcomplete
and either followargcomplete’s instructions for enabling global completion, or add the followingline to your .bash_profile
(this method usually works more reliably):Here are a few examples of typical usages:
Some add-ons are available for pymatgen today: Mac miller good news mp3 download.
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- The pymatgen-db add-onprovides tools to create databases of calculated run data using pymatgen.
- The custodian package provides a JIT job management and errorcorrection for calculations.
- The pymatgen-diffusionby the Materials Virtual Lab provides additional useful analyses fordiffusion in materials.
Contributing¶
Pymatgen is developed by a team of volunteers. It is started by a teamcomprising of MIT and Lawrence Berkeley National Laboratory staff to be arobust toolkit for materials researchers to perform advanced manipulations ofstructures and analyses.
For pymatgen to continue to grow in functionality and robustness, we rely onother volunteers to develop new analyses and report and fix bugs. We welcomeanyone to use our code as-is, but if you could take a few moment to give backto pymatgen in some small way, it would be greatly appreciated. A benefit ofcontributing is that your code will now be used by other researchers who usepymatgen, and we will include an acknowledgement to you (and any relatedpublications) in pymatgen.
Reporting bugs¶
A simple way that anyone can contribute is simply to report bugs and issuesto the developing team. Please report any bugs and issues at pymatgen’sGithub Issues page.For help with any pymatgen issue, consult Stack Overflow and ifyou 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. Download picture collage maker for mac. Thebest way for anyone to develop pymatgen is by adopting the collaborativeGithub workflow (see contributing page).
How to cite pymatgen¶
If you use pymatgen in your research, please consider citing the followingwork:
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. ComputationalMaterials 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 thereferences page for citation info.
License¶
Pymatgen is released under the MIT License. The terms of the license are asfollows:
About the Team¶
Shyue Ping Ong of the Materials Virtual Lab started Pymatgen in 2011, and isstill the project lead.
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The Pymatgen Development Team is the set of all contributors to thepymatgen project, including all subprojects.
The full list of contributors are listed in the team page.
Copyright Policy¶
Pymatgen uses a shared copyright model. Each contributor maintains copyrightover their contributions to pymatgen. But, it is important to note that thesecontributions are typically only changes to the repositories. Thus, thepymatgen source code, in its entirety is not the copyright of anysingle person or institution. Instead, it is the collective copyright of theentire pymatgen Development Team. If individual contributors want to maintain arecord of what changes/contributions they have specific copyright on, theyshould indicate their copyright in the commit message of the change, whenthey commit the change to one of the pymatgen repositories.
With this in mind, the following banner should be used in any source code fileto indicate the copyright and license terms:
Indices and tables¶
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