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 this exercise we will see how to create databases and how to 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 silicon, germanium and carbon in the diamond crystal structure for which ASE already knows the lattice constants:

$ ase build -x diamond Si
$ ase build -x diamond Ge
$ ase build -x diamond C

This creates three files: Si.json, Ge.json and C.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 Si.json C.json Ge.json database.db

This has created an ASE database name database.db.


Create your own set of 3 interesting materials either using the ase build command or the techniques from the exercise “Crystals and band structure” and convert them into a ASE database named database.db. You can also build Si, Ge and C like we do here.

Inspecting a database

We can inspect the database using the db command:

$ ase db database.db

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 Si

which will show all structures containing silicon. To see the details of a particular row we can do:

$ ase db database.db Si -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@Si

where everything after the @ is interpreted as a query.


Create an additional structure by any means and of you choice and add it to the existing ASE database database.db. Hint: Inspect ase db help to figure out how to do this.

Opening a database in Python

Suppose we want do something more advanced with each row in the database. In this case a Python script would be more suited for our needs. To open a database we can use the ase.database.connect method which returns a database object from which we can make selections:

from ase.db import connect

db = connect('database.db')
for row in
    atoms = row.toatoms()

We can make selections in the database using 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 ase.Atoms objects is retrieved by using the row.toatoms() method.


A general 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')

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 =[0]

will show all attributes of the row object.


Using a python script, print the formula for each row in your database.

Write new entries to a database using Python

A new entry in the database can be written using the write() method of a database object.


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.

CAUTION: To relax crystals you have to specify that the cell parameters should be relaxed as well. This is done by wrapping ase.constraints.ExpCellFilter around the atoms object like:

filter = ExpCellFilter(atoms)

and feeding filter into the optimization routine see help(ExpCellFilter) for more explanation.

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.


Loop through all materials in the database and make a self consistent calculation using GPAW in plane wave mode for all materials. Then use the ase.dft.bandgap.bandgap() method to calculate the bandgap of the materials and store it under the bandgap keyword.

When you are done with the exercise inspect your 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++

Browsing data

The database can also be visualized in a browser by using:

$ ase database database.db -w
$ firefox

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

Adsorbates on metals

When you are done with this introductory exercise we encourage you to follow the online ASE-DB tutorial at


from pathlib import Path

from gpaw import GPAW, PW

from import bulk
from ase.constraints import ExpCellFilter
from ase.db import connect
from ase.dft.bandgap import bandgap
from ase.optimize import BFGS

if Path('database.db').is_file():

structures = ['Si', 'Ge', 'C']
db = connect('database.db')

for f in structures:

for row in
    atoms = row.toatoms()
    calc = GPAW(mode=PW(400),
                kpts=(4, 4, 4),
                txt=f'{row.formula}-gpaw.txt', xc='LDA')
    atoms.calc = calc
    filter = ExpCellFilter(atoms)
    opt = BFGS(filter)
    db.write(atoms=atoms, relaxed=True)

for row in
    atoms = row.toatoms()
    calc = GPAW(mode=PW(400),
                kpts=(4, 4, 4),
                txt=f'{row.formula}-gpaw.txt', xc='LDA')
    atoms.calc = calc
    bg, _, _ = bandgap(calc=atoms.calc)
    db.update(, bandgap=bg)