Package: pymatgen-db
Pymatgen-db is a database add-on for the Python Materials Genomics (pymatgen)
materials analysis library. It enables the creation of Materials
Project-styleMongoDBdatabases 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 changeFrom 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-dbStable versionThe easiest way to install pymatgen-db on any system is to use pip, as follows:pip install pymatgen-dbRequirementsAll 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 onPyPI.Python 3.7+ required.Pymatgen 2022+, including all dependencies associated with it. Please refer
to thepymatgen docsfor detailed
installation instructions.Pymongo 3.3+: For interfacing with MongoDb.MongoDB 2.2+: Get it at theMongoDBwebsite.UsageA 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 --helpInitial setupThe first step is to install and setup MongoDB on a server of your choice.
TheMongoDB manualis 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 theMongoDBwebsite, and then running the
following commands:# For Mac and Linux OS.
mkdir test_db && mongod --dbpath test_dbThis 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 theMongoDB
manualwhen 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.jsonThis 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 followingsample.Inserting calculationsTo 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_nameA sample run has been provided fordownloadfor testing
purposes. Unzip the file and run the above command in the directory.Querying a databaseSometimes, 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_atomFor 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-dbCurrently, 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-dbIf 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