.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "examples_generated/tutorials/database.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_examples_generated_tutorials_database.py: .. _intro_ase_db: Introduction to ASE databases ============================= ASE has its own database format that can be used for storing and retrieving atoms (and associated data) in a compact and convenient way. In the following, we will create databases and interact with them through python scripts and the command line. Setting up a database --------------------- To construct a database we first need some atomic structures so let's quickly create some. As you have seen the ASE command line tool provides many convenient commands and in particular we can use the ``build`` command to create some atomic structures. Remember, if you are unsure how to use a particular command you can always append ``-h`` to the particular command (ie. ``ase build -h``) to see the help for that particular command. We choose to build aluminium, copper and gold in the diamond crystal structure for which ASE already knows the lattice constants: :: $ ase build -x fcc Al $ ase build -x fcc Cu $ ase build -x fcc Au This creates three files: :file:`Al.json`, :file:`Cu.json` and :file:`Au.json`. If you want to, you can inspect them with ASE's ``gui`` command, however we want to construct a database containing these structures. To do this we can use ``convert``:: $ ase convert Al.json Cu.json Au.json database.db This has created an ASE database name :file:`database.db`. Additionally, one can use the ``ase build`` command to build Si, Ge and C and convert them into a ASE database named :file:`database.db` using the following: .. GENERATED FROM PYTHON SOURCE LINES 42-60 .. code-block:: Python from pathlib import Path from ase.build import bulk from ase.calculators.emt import EMT from ase.db import connect from ase.filters import FrechetCellFilter from ase.optimize import BFGS dbfile = Path('database.db') dbfile.unlink(missing_ok=True) structures = ['Al', 'Cu', 'Au'] db = connect(dbfile) for structure in structures: db.write(bulk(structure)) .. GENERATED FROM PYTHON SOURCE LINES 61-66 Inspecting a database on the command line ----------------------------------------- We can inspect the database using the ``db`` command:: $ ase db database.db .. GENERATED FROM PYTHON SOURCE LINES 68-92 which will display three entries, one for each structure. From this point it is advised to bring up the help for the ``db`` command every time you need it. From the help we can see that it is possible to make selections (queries in database lingo) in the database by:: $ ase db database.db Al which will show all structures containing silicon. To see the details of a particular row we can do:: $ ase db database.db Al -l From which we can get an overview of the stored data. We can also view all structures in a database using:: $ ase gui database.db or if we want to view a single one we can do:: $ ase gui database.db@Al where everything after the @ is interpreted as a query. .. GENERATED FROM PYTHON SOURCE LINES 95-99 Opening a database using a Python script ---------------------------------------- To open a database using Python, we can use the :class:`ase.database.connect` method which returns a database object from which we can make selections: .. GENERATED FROM PYTHON SOURCE LINES 99-105 .. code-block:: Python db = connect(dbfile) for row in db.select(): atoms = row.toatoms() print(atoms) .. rst-class:: sphx-glr-script-out .. code-block:: none Atoms(symbols='Al', pbc=True, cell=[[0.0, 2.025, 2.025], [2.025, 0.0, 2.025], [2.025, 2.025, 0.0]]) Atoms(symbols='Cu', pbc=True, cell=[[0.0, 1.805, 1.805], [1.805, 0.0, 1.805], [1.805, 1.805, 0.0]]) Atoms(symbols='Au', pbc=True, cell=[[0.0, 2.04, 2.04], [2.04, 0.0, 2.04], [2.04, 2.04, 0.0]]) .. GENERATED FROM PYTHON SOURCE LINES 106-138 We can make selections in the database using ``db.select(some_selection)`` which returns all rows matching ``some_selection``. In this case ``some_selection`` was omitted which means that we select all rows in the database. For each row the associated :class:`ase.Atoms` objects is retrieved by using the ``row.toatoms()`` method. .. admonition:: Hint In order to see the documentation for a particular python function import it and use the ``help`` function. For example :: from ase.db import connect db = connect('database.db') help(db.select) will show the documentation for the select method of the database object. Another useful function is ``dir`` which shows all attributes of a python object. For example :: from ase.db import connect db = connect('database.db') row = db.select(id=1)[0] dir(row) will show all attributes of the row object. Using a python script, print the formula for each row in your database. .. GENERATED FROM PYTHON SOURCE LINES 140-160 Write new entries to a database using Python -------------------------------------------- Next, we loop through all materials, relax them (see exercise "Structure Optimization") and save the relaxed structure as a new entry in the database with an added column relaxed equal to ``True`` that we can use later for selecting only these materials. A new entry in the database can be written using the ``write()`` method of a database object. .. admonition:: Hint To relax crystals we have to specify that the cell parameters should be relaxed as well. This is done by wrapping :class:`ase.filters.FrechetCellFilter` around the atoms object like :: filter = FrechetCellFilter(atoms) and feeding ``filter`` into the optimization routine see ``help(FrechetCellFilter)`` for more explanation. .. GENERATED FROM PYTHON SOURCE LINES 160-171 .. code-block:: Python for row in db.select(): atoms = row.toatoms() calc = EMT() atoms.calc = calc atoms.get_stress() filter = FrechetCellFilter(atoms) opt = BFGS(filter) opt.run(fmax=0.05) db.write(atoms=atoms, relaxed=True) .. rst-class:: sphx-glr-script-out .. code-block:: none Step Time Energy fmax BFGS: 0 23:46:21 -0.001502 0.161794 BFGS: 1 23:46:21 -0.002532 0.135189 BFGS: 2 23:46:21 -0.004882 0.002382 Step Time Energy fmax BFGS: 0 23:46:21 -0.005682 0.160183 BFGS: 1 23:46:21 -0.006560 0.095504 BFGS: 2 23:46:21 -0.007036 0.001838 Step Time Energy fmax BFGS: 0 23:46:21 0.002606 0.308859 BFGS: 1 23:46:21 0.000034 0.077987 BFGS: 2 23:46:21 -0.000135 0.002381 .. GENERATED FROM PYTHON SOURCE LINES 172-176 Here, we are using EMT, which is a force-field calculator. This is not accurate enough for production level computations. For production level computations, self consistent calculations, such as using GPAW, have to be used. .. GENERATED FROM PYTHON SOURCE LINES 178-184 Adding data to existing database -------------------------------- Now we want to calculate some data and include the data in the database which can be done using the ``update`` method of the database object. Then use the ``atoms.get_potential_energy()`` method to calculate the energy of the materials and store it under the ``energy`` keyword. .. GENERATED FROM PYTHON SOURCE LINES 184-193 .. code-block:: Python for row in db.select(relaxed=True): atoms = row.toatoms() calc = EMT() atoms.calc = calc e = atoms.get_potential_energy() db.update(row.id, data={'energy': e}) .. GENERATED FROM PYTHON SOURCE LINES 194-199 Now, we can inspect the database again using the ``ase db`` command. To see the new column ``bandgap`` you can display all columns using the ``-c++`` option:: $ ase db database -c++ .. GENERATED FROM PYTHON SOURCE LINES 201-213 Browsing data ------------- The database can also be visualized in a browser by using:: $ ase database database.db -w $ firefox http://0.0.0.0:5000/ This opens a local webserver which can be opened in firefox like above. The layout can be customized further than our simple example however this would probably be too much for now. To see a more advanced example of such a web interfaced database in action you can check out the 2D database https://cmrdb.fysik.dtu.dk/c2db. .. GENERATED FROM PYTHON SOURCE LINES 215-219 Advanced tutorial ----------------- An additional tutorial using the ASE databases for adsorbates on metals can be found at https://ase-lib.org/tutorials/db/db.html. .. _sphx_glr_download_examples_generated_tutorials_database.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: database.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: database.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: database.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_