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https://github.com/materialsproject/pymatgen-db

Pymatgen-db provides an addon to the Python Materials Genomics (pymatgen) library (https://pypi.python.org/pypi/pymatgen) that allows the creation of Materials Project-style databases for management of materials data.
https://github.com/materialsproject/pymatgen-db

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Pymatgen-db provides an addon to the Python Materials Genomics (pymatgen) library (https://pypi.python.org/pypi/pymatgen) that allows the creation of Materials Project-style databases for management of materials data.

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README 

        
Pymatgen-db is a database add-on for the Python Materials Genomics (pymatgen)

materials analysis library. It enables the creation of Materials

Project-style `MongoDB`_ databases for management of materials data. A query

engine is also provided to enable the easy translation of MongoDB docs to

useful pymatgen objects for analysis purposes.



Major change

------------



From v2021.5.13, pymatgen-db is now a proper namespace add-on to pymatgen. In

other words, you no longer import from matgendb but rather pymatgen.db.



Getting pymatgen-db

===================



Stable version

--------------



The easiest way to install pymatgen-db on any system is to use pip, as follows::



    pip install pymatgen-db



Requirements

============



All required python dependencies should be automatically taken care of if you

install pymatgen-db using easy_install or pip. Otherwise, these packages should

be available on `PyPI `_.



1. Python 3.7+ required.

2. Pymatgen 2022+, including all dependencies associated with it. Please refer

   to the `pymatgen docs `_ for detailed

   installation instructions.

3. Pymongo 3.3+: For interfacing with MongoDb.

4. MongoDB 2.2+: Get it at the `MongoDB`_ website.



Usage

=====



A powerful command-line script (mgdb) provides most of the access to many of

the features in pymatgen-db, including db initialization, insertion of data,

running the materials genomics ui, etc. To see all options available, type::



    mgdb --help



Initial setup

-------------



The first step is to install and setup MongoDB on a server of your choice.

The `MongoDB manual`_ is an excellent place to start. For the purposes of

testing out the tools here, you may simply download the binary distributions

corresponding to your OS from the `MongoDB`_ website, and then running the

following commands::



    # For Mac and Linux OS.

    mkdir test_db && mongod --dbpath test_db



This will create a test database and start the Mongo daemon. Once you are

done with testing, you can simply press Ctrl-C to stop the server and delete

the "test_db" folder. Running a Mongo server this way is insecure as Mongo

does not enable authentication by default. Please refer to the `MongoDB

manual`_ when setting up your production database.



After your server is up, you should create a database config file by running

the following command::



    mgdb init -c db.json



This will prompt you for a few parameters to create a database config file,

which will make it much easier to use mgdb in future. Note that the config file

name can be anything of your choice, but using "db.json" will allow you to use

mgdb without explicitly specifying the filename in future. If you are just

testing using the test database, simply hit Enter to accept the defaults for

all settings.



For more advanced use of the "db.json" config file (e.g., specifying aliases,

defaults, etc., please refer to the following `sample

`_.



Inserting calculations

----------------------



To insert an entire directory of runs (where the topmost directory is

"dir_name") into the database, use the following command::



    # Note that "-c db.json" may be omitted if the config filename is the

    # current directory under the default filename of db.json.



    mgdb insert -c db.json dir_name



A sample run has been provided for `download

`_ for testing

purposes. Unzip the file and run the above command in the directory.



Querying a database

-------------------



Sometimes, more fine-grained querying is needed (e.g., for subsequent

postprocessing and analysis).



The mgdb script allows you to make simple queries from the command line::



    # Query for the task id and energy per atom of all calculations with

    # formula Li2O. Note that the criteria has to be specified in the form of

    # a json string. Note that "-c db.json" may be omitted if the config

    # filename is the current directory under the default filename of db.json.



    mgdb query -c db.json --crit '{"pretty_formula": "Li2O"}' --props task_id energy_per_atom



For more advanced queries, you can use the QueryEngine class for which an

alias is provided at the root package. Some examples are as follows::



    >>> from pymatgen.db import QueryEngine

    # Depending on your db.json, you may need to supply keyword args below

    # for `port`, `database`, `collection`, etc.

    >>> qe = QueryEngine()



    #Print the task id and formula of all entries in the database.

    >>> for r in qe.query(properties=["pretty_formula", "task_id"]):

    ...     print "{task_id} - {pretty_formula}".format(**r)

    ...

    12 - Li2O



    # Get a pymatgen Structure from the task_id.

    >>> structure = qe.get_structure_from_id(12)



    # Get pymatgen ComputedEntries using a criteria.

    >>> entries = qe.get_entries({})



The language follows very closely to pymongo/MongoDB syntax, except that

QueryEngine provides useful aliases for commonly used fields as well as

translation to commonly used pymatgen objects like Structure and

ComputedEntries.



Extending pymatgen-db

---------------------



Currently, pymatgen-db is written with standard VASP runs in mind. However,

it is perfectly extensible to any kind of data, e.g., other kinds of VASP runs

(bandstructure, NEB, etc.) or just any form of data in general. Developers

looking to adapt pymatgen-db for other purposes should look at the

VaspToDbTaskDrone class as an example and write similar drones for their

needs. The QueryEngine can generally be applied to any Mongo collection,

with suitable specification of aliases if desired.



How to cite pymatgen-db

=======================



If you use pymatgen and pymatgen-db 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

    `_



.. _`MongoDB` : http://www.mongodb.org/

.. _`Github repo` : https://github.com/materialsproject/pymatgen-db

.. _`MongoDB manual` : http://docs.mongodb.org/manual/