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 five 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.

MySQL:

Server based database.

MariaDB:

Server based database.

The JSON and SQLite3 back-ends work “out of the box”, whereas PostgreSQL, MySQL and MariaDB requires a server (See Running a PostgreSQL server or Running a MySQL 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|user |formula|calculator|energy| fmax|pbc|charge| mass
 1| 2s|jensj|H2     |emt       | 1.419|9.803|FFF| 0.000|2.016
 2| 2s|jensj|H2     |emt       | 1.071|0.000|FFF| 0.000|2.016
 3| 2s|jensj|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        
Uniqe row ID |2            
Total energy |1.071 eV     
Maximum force|0.000 eV/Ang 
Mass         |2.016 au     
Age          |2.684 seconds

=============================== Miscellaneous ================================
Items              |                                
ASE-calculator name|emt                             
Charge             |0.000 |e|                       
Chemical formula   |H2                              
relaxed            |True                            
Random (unique) ID |2644263a099e348d7febdc971536699c
Username           |jensj                           

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)  # doctest: +SKIP
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)  # doctest: +SKIP
-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]))  # doctest: +SKIP
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.

property key_value_pairs

Return dict of key-value pairs.

count_atoms()[source]

Count atoms.

Return dict mapping chemical symbol strings to number of atoms.

property constraints

List of constraints.

property data

Data dict.

property natoms

Number of atoms.

property formula

Chemical formula string.

property symbols

List of chemical symbols.

property fmax

Maximum atomic force.

property constrained_forces

Forces after applying constraints.

property smax

Maximum stress tensor component.

property mass

Total mass.

property volume

Volume of unit cell.

property 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, if you want to update() many rows, you should do it in one transaction:

with db:
    for id in ...:
        db.update(id, foo='bar')

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, id=id, name=name)

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, id=None, 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).

id: int

Overwrite existing row.

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(id, atoms=None, delete_keys=[], data=None, **add_key_value_pairs)[source]

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

id: int

ID of row to update.

atoms: Atoms object

Optionally update the Atoms data (positions, cell, …).

data: dict

Data dict to be added to the existing data.

delete_keys: list of str

Keys to remove.

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, columns='all', **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.

columns: ‘all’ or list of str

Specify which columns from the SQL table to include. For example, if only the row id and the energy is needed, queries can be speeded up by setting columns=[‘id’, ‘energy’].

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

\(\text{eV/Ang}^3\)

charge

Charge

Charge

|e|

mass

Mass

Mass

au

magmom

Magnetic moment

Magnetic moment

au

unique_id

Unique ID

Random (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

External Tables

If the number of key_value_pairs becomes large, ASE DB offers an alternative way of storing them. Internally ASE can create a dedicated table to store groups of key_value_pairs. You can store a group of key_value_pairs associated with thermodynamics in a separate table named thermodynamics by:

>>> atoms = Atoms()
>>> id = db.write(atoms, external_tables={'thermodynamics': {'heat_capacity': 3.0, 'internal_energy': -2.0}})

Values stored in external tables can be accessed using:

>>> row = db.get(id=id)
>>> heat_capacity = row['thermodynamics']['heat_capacity']
>>> internal_energy = row['thermodynamics']['internal_energy']

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.

Running a MySQL server

ASE DB can also be run with a MySQL server. First, we need to get the MySQL server up and running. There are many online resources describing how to to that, but on a Ubuntu system the following should work:

$ sudo apt-get install mysql-server
$ sudo mysql_secure_installation

Then we need to check if the server is running:

$ systemctl status mysql.service

if it is not running you can start the service by:

$ systemctl start mysql.service

Note that on some Linux distributions mysql.service should be replaced by mysqld.service.

Once the service is running, we can enter the MySQL shell:

$ mysql -u root -p

where we assume that there is a user named root, that will be prompted for a password. Now, we can create a user:

mysql> CREATE USER 'ase'@'localhost' IDENTIFIED BY 'strongPassword';

and then a database for our project:

mysql> CREATE DATABASE my_awesome_project;

We need to give the ase user privileges to edit this database:

mysql> GRANT ALL PRIVILEGES ON my_awesome_project.* TO 'ase'@'localhost' IDENTIFIED BY 'strongPassword';

From a Python script we can now connect to the database via

>>> mysql_url = 'mysql://ase:strongPassword@localhost:3306/my_awesome_project'
>>> connect(mysql_url)  # doctest: +SKIP