A database for atoms

ASE has its own database that can be used for storing and retrieving atoms and associated data in a compact and convenient way.

There are currently three back-ends:

JSON:
Simple human-readable text file with a .json extension.
SQLite3:
Self-contained, server-less, zero-configuration database. Lives in a file with a .db extension.
PostgreSQL:
Server based database.

The JSON and SQLite3 back-ends work “out of the box”, whereas PostgreSQL requires a Running a PostgreSQL server.

There is a command-line tool called ase db that can be used to query and manipulate databases and also a Python interface.

What’s in the database?

Every row in the database contains:

  • all the information stored in the Atoms object (positions, atomic numbers, …)
  • calculator name and parameters (if a calculator is present)
  • already calculated properties such as energy and forces (if a calculator is present)
  • key-value pairs (for finding the calculation again)
  • an integer ID (unique for each database) starting with 1 and always increasing for each new row
  • a unique ID which is a 128 bit random number which should be globally unique (at least in the lifetime of our universe)
  • constraints (if present)
  • user-name
  • creation and modification time

ase db

The ase db command-line tool can be used to query databases and for manipulating key-value pairs. Try:

$ ase db --help

Example: Show all rows of SQLite database abc.db:

$ ase db abc.db
id|age|formula|calculator|energy| fmax|pbc|charge| mass
 1| 0s|H2     |emt       | 1.419|9.803|FFF| 0.000|2.016
 2| 0s|H2     |emt       | 1.071|0.000|FFF| 0.000|2.016
 3| 0s|H      |emt       | 3.210|0.000|FFF| 0.000|1.008
Rows: 3
Keys: relaxed

Show all details for a single row:

$ ase db abc.db relaxed=1 -l
H2:
============================== Basic properties ==============================
Unit cell in Ang:
axis|periodic|          x|          y|          z
   1|      no|      0.000|      0.000|      0.000
   2|      no|      0.000|      0.000|      0.000
   3|      no|      0.000|      0.000|      0.000
Lengths:      0.000     0.000     0.000
Angles:      90.000    90.000    90.000

Key Value Pairs:
name               |value
Uniqe row ID       |2 
Chemical formula   |H2 
Time since creation|0.509 seconds 


Forces in ev/Ang:
   0|H |     0.000|     0.000|    -0.000
   1|H |    -0.000|     0.000|     0.000

=============================== Miscellaneous ================================
Items:
name               |value
ASE-calculator name|emt 
Charge             |0.000 |e|
Total energy       |1.071 eV
Maximum force      |0.000 eV/Ang
Mass               |2.016 au
relaxed            |True 
unique id          |aad4c698ea820734e0333983f36a4b78 

Data: abc 

Querying

Here are some example query strings:

Cu contains copper
H<3 less than 3 hydrogen atoms
Cu,H<3 contains copper and has less than 3 hydrogen atoms
H2O At least two hydrogens and at least one oxygen
formula=H2O Exactly two hydrogens and one oxygen
v3 has ‘v3’ key
abc=bla-bla has key ‘abc’ with value ‘bla-bla’
v3,abc=bla-bla both of the above
calculator=nwchem calculations done with NWChem
2.2<bandgap<3.0 ‘bandgap’ key has value between 2.2 and 3.0
natoms>=10 10 or more atoms
id=2345 specific id
age<1h not older than 1 hour
age>1y older than 1 year
pbc=TTT Periodic boundary conditions along all three axes
pbc=TTF Periodic boundary conditions along the first two axes (F=False, T=True)

These names are special:

id integer identifier
natoms number of atoms
pbc Periodic boundary conditions
formula formula
energy potential energy
charge total charge
magmom total magnetic moment
calculator name of calculator
user who did it
age age of calculation (use s, m, h, d, w, M and y for second, minute, hour, day, week, month and year respectively)

Integration with other parts of ASE

ASE’s ase.io.read() function can also read directly from databases:

>>> from ase.io import read
>>> a = read('abc.db@42')
>>> a = read('abc.db@id=42')  # same thing
>>> b = read('abc.db@v3,abc=H')

Also the ASE’s GUI program can read from databases using the same syntax.

Browse database with your web-browser

You can use your web-browser to look at and query databases like this:

$ ase db abc.db -w
$ firefox http://0.0.0.0:5000/

Click individual rows to see details. See the CMR web-page for an example of how this works.

Python Interface

First, we connect() to the database:

>>> from ase.db import connect
>>> db = connect('abc.db')

or

>>> import ase.db
>>> db = ase.db.connect('abc.db')

Let’s do a calculation for a hydrogen molecule and write some results to a database:

>>> from ase import Atoms
>>> from ase.calculators.emt import EMT
>>> h2 = Atoms('H2', [(0, 0, 0), (0, 0, 0.7)])
>>> h2.calc = EMT()
>>> h2.get_forces()
array([[ 0.   ,  0.   , -9.803],
       [ 0.   ,  0.   ,  9.803]])

Write a row to the database with a key-value pair ('relaxed', False):

>>> db.write(h2, relaxed=False)
1

The write() method returns an integer id.

Do one more calculation and write results:

>>> from ase.optimize import BFGS
>>> BFGS(h2).run(fmax=0.01)  
BFGS:   0  12:49:25        1.419427       9.8029
BFGS:   1  12:49:25        1.070582       0.0853
BFGS:   2  12:49:25        1.070544       0.0236
BFGS:   3  12:49:25        1.070541       0.0001
>>> db.write(h2, relaxed=True)
2

Loop over selected rows using the select() method:

>>> for row in db.select(relaxed=True):
...     print(row.forces[0, 2], row.relaxed)  
-9.8029057329 False
-9.2526347333e-05 True

The select() method will generate Row objects that one can loop over.

Write the energy of an isolated hydrogen atom to the database:

>>> h = Atoms('H')
>>> h.calc = EMT()
>>> h.get_potential_energy()
3.21
>>> db.write(h)
3

Select a single row with the get() method:

>>> row = db.get(relaxed=1, calculator='emt')
>>> for key in row:
...    print('{0:22}: {1}'.format(key, row[key]))  
pbc                   : [False False False]
relaxed               : True
calculator_parameters : {}
user                  : jensj
mtime                 : 15.3439399027
calculator            : emt
ctime                 : 15.3439399027
positions             : [[ ... ]]
id                    : 2
cell                  : [[ 0.  0.  0.] [ 0.  0.  0.] [ 0.  0.  0.]]
forces                : [[ ... ]]
energy                : 1.07054126233
unique_id             : bce90ff3ea7661690b54f9794c1d7ef6
numbers               : [1 1]

Calculate the atomization energy and update() a row in the database:

>>> e2 = row.energy
>>> e1 = db.get(H=1).energy
>>> ae = 2 * e1 - e2
>>> print(ae)
5.34945873767
>>> id = db.get(relaxed=1).id
>>> db.update(id, atomization_energy=ae)
1

Delete a single row:

>>> del db[db.get(relaxed=0).id]

or use the delete() method to delete several rows.

Description of a row

The first 9 keys (from “id” to “positions”) are always present — the rest may be there:

key description datatype shape
id Local database id int  
unique_id Globally unique hexadecimal id str  
ctime Creation time float  
mtime Modification time float  
user User name str  
numbers Atomic numbers int (N,)
pbc Periodic boundary condition flags bool (3,)
cell Unit cell float (3, 3)
positions Atomic positions float (N, 3)
initial_magmoms Initial atomic magnetic moments float (N,)
initial_charges Initial atomic charges float (N,)
masses Atomic masses float (N,)
tags Tags int (N,)
momenta Atomic momenta float (N, 3)
constraints Constraints list of dict  
energy Total energy float  
forces Atomic forces float (N, 3)
stress Stress tensor float (6,)
dipole Electrical dipole float (3,)
charges Atomic charges float (N,)
magmom Magnetic moment float  
magmoms Atomic magnetic moments float (N,)
calculator Calculator name str  
calculator_parameters Calculator parameters dict  

Extracting Atoms objects from the database

If you want an Atoms object insted of an AtomsRow object, you should use the get_atoms() method:

>>> h2 = db.get_atoms(H=2)

or if you want the original EMT calculator attached:

>>> h2 = db.get_atoms(H=2, attach_calculator=True)

Add additional data

When you write a row to a database using the write() method, you can add key-value pairs where the values can be strings, floating point numbers, integers and booleans:

>>> db.write(atoms, functional='LDA', distance=7.2)

More complicated data can be written like this:

>>> db.write(atoms, ..., data={'parents': [7, 34, 14], 'stuff': ...})

and accessed like this:

>>> row = db.get(...)
>>> row.data.parents
[7, 34, 14]

Row objects

There are three ways to get at the columns of a row:

  1. as attributes (row.key)
  2. indexing (row['key'])
  3. the get() method (row.get('key'))

The first two will fail if there is no key column whereas the last will just return None in that case. Use row.get('key', ...) to use another default value.

class ase.db.row.AtomsRow(dct)[source]
get(key, default=None)[source]

Return value of key if present or default if not.

key_value_pairs

Return dict of key-value pairs.

count_atoms()[source]

Count atoms.

Return dict mapping chemical symbol strings to number of atoms.

constraints

List of constraints.

data

Data dict.

natoms

Number of atoms.

formula

Chemical formula string.

symbols

List of chemical symbols.

fmax

Maximum atomic force.

constrained_forces

Forces after applying constraints.

smax

Maximum stress tensor component.

mass

Total mass.

volume

Volume of unit cell.

charge

Total charge.

toatoms(attach_calculator=False, add_additional_information=False)[source]

Create Atoms object.

Writing and updating many rows efficiently

If you do this:

db = connect('mols.db')
for mol in molecules:
    db.write(mol, ...)

the database will make sure that each molecule is written to permanent starage (typically a harddisk) before it moves on to the next molecule. This can be quite slow. To speed this up, you can write all the molecules in a single transaction like this:

with connect('mols.db') as db:
    for mol in molecules:
        db.write(mol, ...)

When the for-loop is done, the database will commit (or roll back if there was an error) the transaction.

Similarly, the update() method will do up to block_size=1000 rows in one transaction:

# slow:
for row in db.select(...):
    db.update(row.id, foo='bar')  # a single id
# faster:
ids = [row.id for row in db.select(...)]
db.update(ids, foo='bar')  # list of id's

Writing rows in parallel

Say you want to run a series of jobs and store the calculations in one database:

for name in many_molecules:
    mol = read(name)
    calculate_something(mol)
    db.write(mol, name=name)

With four extra lines (see the reserve() method):

for name in many_molecules:
    id = db.reserve(name=name)
    if id is None:
        continue
    mol = read(name)
    calculate_something(mol)
    db.write(mol, name=name)
    del db[id]

you will be able to run several jobs in parallel without worrying about two jobs trying to do the same calculation. The reserve() method will write an empty row with the name key and return the ID of that row. Other jobs trying to make the same reservation will fail. While the jobs are running, you can keep an eye on the ongoing (reserved) calculations by identifying empty rows:

$ ase db many_results.db natoms=0

More details

Use this function for getting a connection to a database:

ase.db.core.connect(name, type='extract_from_name', create_indices=True, use_lock_file=True, append=True, serial=False)[source]

Create connection to database.

name: str
Filename or address of database.
type: str
One of ‘json’, ‘db’, ‘postgresql’, (JSON, SQLite, PostgreSQL). Default is ‘extract_from_name’, which will guess the type from the name.
use_lock_file: bool
You can turn this off if you know what you are doing …
append: bool
Use append=False to start a new database.

Here is a description of the database object:

class ase.db.core.Database(filename=None, create_indices=True, use_lock_file=False, serial=False)[source]

Base class for all databases.

Database object.

serial: bool
Let someone else handle parallelization. Default behavior is to interact with the database on the master only and then distribute results to all slaves.
write(atoms, key_value_pairs={}, data={}, **kwargs)[source]

Write atoms to database with key-value pairs.

atoms: Atoms object
Write atomic numbers, positions, unit cell and boundary conditions. If a calculator is attached, write also already calculated properties such as the energy and forces.
key_value_pairs: dict
Dictionary of key-value pairs. Values must be strings or numbers.
data: dict
Extra stuff (not for searching).

Key-value pairs can also be set using keyword arguments:

connection.write(atoms, name='ABC', frequency=42.0)

Returns integer id of the new row.

reserve(**key_value_pairs)[source]

Write empty row if not already present.

Usage:

id = conn.reserve(key1=value1, key2=value2, ...)

Write an empty row with the given key-value pairs and return the integer id. If such a row already exists, don’t write anything and return None.

update(ids, delete_keys=[], block_size=1000, **add_key_value_pairs)[source]

Update and/or delete key-value pairs of row(s).

ids: int or list of int
ID’s of rows to update.
delete_keys: list of str
Keys to remove.
block_size: int
Block-size for each transaction.

Use keyword arguments to add new key-value pairs.

Returns number of key-value pairs added and removed.

metadata

Dictionary

get_atoms(selection=None, attach_calculator=False, add_additional_information=False, **kwargs)[source]

Get Atoms object.

selection: int, str or list
See the select() method.
attach_calculator: bool
Attach calculator object to Atoms object (default value is False).
add_additional_information: bool
Put key-value pairs and data into Atoms.info dictionary.

In addition, one can use keyword arguments to select specific key-value pairs.

get(selection=None, **kwargs)[source]

Select a single row and return it as a dictionary.

selection: int, str or list
See the select() method.
select(selection=None, filter=None, explain=False, verbosity=1, limit=None, offset=0, sort=None, include_data=True, **kwargs)[source]

Select rows.

Return AtomsRow iterator with results. Selection is done using key-value pairs and the special keys:

formula, age, user, calculator, natoms, energy, magmom and/or charge.
selection: int, str or list

Can be:

  • an integer id
  • a string like ‘key=value’, where ‘=’ can also be one of ‘<=’, ‘<’, ‘>’, ‘>=’ or ‘!=’.
  • a string like ‘key’
  • comma separated strings like ‘key1<value1,key2=value2,key’
  • list of strings or tuples: [(‘charge’, ‘=’, 1)].
filter: function
A function that takes as input a row and returns True or False.
explain: bool
Explain query plan.
verbosity: int
Possible values: 0, 1 or 2.
limit: int or None
Limit selection.
offset: int
Offset into selected rows.
sort: str
Sort rows after key. Prepend with minus sign for a decending sort.
include_data: bool
Use include_data=False to skip reading data from rows.
count(selection=None, **kwargs)[source]

Count rows.

See the select() method for the selection syntax. Use db.count() or len(db) to count all rows.

delete(ids)[source]

Delete rows.

Metadata

It’s can be useful to add a discription of the data that’s in a database. This description will be used when browsing the database with a web-browser. You can add the desciption using the metadata attribute:

>>> db.metadata = {
...     'title': 'Project 1',
...     'key_descriptions':
...         {'v0': ('Voltage', 'Longer description ...', 'V')},
...     'default_columns': ['id', 'formula', 'v0']}

ASE already knows all about the following keys:

key short description long description unit
id ID Uniqe row ID  
age Age Time since creation  
formula Formula Chemical formula  
user Username Username  
calculator Calculator ASE-calculator name  
energy Energy Total energy eV
fmax Maximum force Maximum force eV/Ang
smax Maximum stress Maximum stress \(\text{eV/Ang}^3\)
pbc PBC Periodic boundary conditions  
charge Charge Charge |e|
mass Mass Mass au
magmom Magnetic moment Magnetic moment au
unique_id Unique ID Unique ID  
volume Volume Volume of unit-cell \(\text{Ang}^3\)

You can also write/read to/from JSON using:

$ ase db proj1.db --set-metadata metadata.json
$ ase db proj1.db --show-metadata > metadata.json

Running a PostgreSQL server

With your PostgreSQL server up and running, you should run the following command as the postgres user:

$ sudo -u postgres psql

Then you create an ‘ase’ user and one database for each project you have:

postgres=# create user ase login password 'pw';
postgres=# create database project1;
postgres=# create database project2;

Show databases and quit:

postgres=# \l
postgres=# \q

You should now be able to query the database using a URI like postgresql://user:pw@host:port/dbname:

$ PROJ1=postgresql://ase:pw@localhost:5432/project1
$ ase db $PROJ1

If you have some data in a data.db SQLite3 file, then you can insert that into the PostgreSQL database like this:

$ ase db data.db --insert-into $PROJ1

Now you can start the Flask-app ase.db.app. You can use Flask’s own web-server or use any WSGI compatible server. We will use Twisted in the example below. Set the $ASE_DB_APP_CONFIG environment variable to point to a Python configuration file containing something similar to this:

ASE_DB_NAMES = ['postgresql://ase:pw@localhost:5432/project1',
                'postgresql://ase:pw@localhost:5432/project2',
                ...]
ASE_DB_HOMEPAGE = '<a href="https://home.page.org">HOME</a>'

and then start the server with:

$ ASE_DB_APP_CONFIG=... twistd web --wsgi=ase.db.app.app --port=tcp:8000

Note

Please review the code carefully before exposing the ase.db.app to the internet or bad things could happen.