The data module

Atomic data

This module defines the following variables:

All of these are lists that should be indexed with an atomic number:

>>> from import atomic_numbers, atomic_names, atomic_masses, covalent_radii
>>> atomic_names[92]
>>> atomic_masses[2]

If you don’t know the atomic number of some element, then you can look it up in the atomic_numbers dictionary:

>>> atomic_numbers['Cu']
>>> covalent_radii[29]

Atomic masses are based on [Meija2016] (same array as atomic_masses_iupac2016).

Standard atomic weights are taken from Table 1: “Standard atomic weights 2013”, with the uncertainties ignored.

For hydrogen, helium, boron, carbon, nitrogen, oxygen, magnesium, silicon, sulfur, chlorine, bromine and thallium, where the weights are given as a range the “conventional” weights are taken from Table 3, and the ranges are given in the source code comments.

The mass of the most stable isotope (in Table 4) is used for elements where there the element has no stable isotopes (to avoid NaNs): Tc, Pm, Po, At, Rn, Fr, Ra, Ac, everything after Np

Atomic masses provided by ASE before 2017 can be accessed in the atomic_masses_legacy member. To recover legacy behaviour an Atoms object can be modified as:

>>> from import atomic_masses_legacy
>>> atoms.set_masses(atomic_masses_legacy[atoms.numbers])

The covalent radii are taken from [Cordeo08].

The source of the van der Waals radii is given in

A newer source of van der Waals radii is given in These radii are taken from [Alvarez13].


Atomic weights of the elements 2013 (IUPAC Technical Report). Meija, J., Coplen, T., Berglund, M., et al. (2016). Pure and Applied Chemistry, 88(3), pp. 265-291. Retrieved 30 Nov. 2016, from doi:10.1515/pac-2015-0305


Covalent radii revisited, Beatriz Cordero, Verónica Gómez, Ana E. Platero-Prats, Marc Revés, Jorge Echeverría, Eduard Cremades, Flavia Barragán and Santiago Alvarez, Dalton Trans., 2008, 2832-2838 DOI:10.1039/B801115J


A cartography of the van der Waals territories, Alvarez, S., Dalton Trans., 2013, 42, 8617-8636, DOI:10.1039/C3DT50599E

How to extract isotope data from NIST[source]

Download isotope data from NIST public website.

Relative atomic masses of individual isotopes their abundance (mole fraction) are compiled into a dictionary. Individual items can be indexed by the atomic number and mass number, e.g. titanium-48:

>>> from import extract_isotope_data
>>> isotopes = extract_isotope_data()
>>> isotopes[22][48]['mass']
>>> isotopes[22][48]['composition']

Molecular data

The G1, G2, and G3-databases are available. Example:

>>> from import molecule
>>> atoms = molecule('H2O')

All molecular members of each database is conveniently contained in a list of strings (g1, g2, g3), ??? and one can look up the experimental atomization energy for each molecule. This is extrapolated from experimental heats of formation at room temperature, using calculated zero-point energies and thermal corrections.


>>> from import get_atomization_energy
>>> get_atomization_energy('H2O')
>>> from ase.units import kcal,mol
>>> get_atomization_energy('H2O')*kcal/mol

where the last line converts the experimental atomization energy of H2O from units of kcal/mol to eV.

S22, s26, and s22x5 data

The s22, s26, and s22x5 databases are available in the s22 module.

Each weakly bonded complex is identified as an entry in a list of strings (s22, s26, s22x5), and is fully created by a ‘create’-function:

>>> from import s22, create_s22_system
>>> sys = s22[0]
>>> sys
>>> atoms = create_s22_system(sys)
>>> atoms.get_chemical_symbols()
['N', 'H', 'H', 'H', 'N', 'H', 'H', 'H']

The coupled-cluster interaction energies for the s22 and s26 systems are retrieved like this:

>>> from import s22, get_interaction_energy_s22
>>> get_interaction_energy_s22(s22[0])

in units of eV. For s22 these are not the original energies, but from more recent work where the same (large) basis set was used for all complexes, yielding more accurate coupled-cluster interaction energies.

The s22x5 database expands on the original s22 data by introducing non-equilibrium geometries for each complex (0.9, 1.0, 1.2, 1.5, and 2.0 times original intermolecular distance). However, these calculations were done in accordance with the methods used in the original s22 work, and so is expected to inherit the same problems with mixed basis set sizes. Assuming the interaction energy error due to this is the same in all 5 geometries for each complex, the default s22x5 interaction energies are therefore corrected with the energy difference between original and newer energies at the original separation.


>>> from import *
>>> sys1 = s22[0]
>>> sys1
>>> atoms1 = create_s22_system(sys1)
>>> sys2 = s22x5[0]
>>> sys2
>>> atoms2 = create_s22_system(sys2)
>>> sys3 = s22x5[1]
>>> sys3
>>> atoms3 = create_s22_system(sys3)
>>> get_interaction_energy_s22(sys1)
>>> get_interaction_energy_s22(sys2)
>>> get_interaction_energy_s22(sys3)
>>> get_interaction_energy_s22x5(sys2)
>>> get_interaction_energy_s22x5(sys3)
>>> get_interaction_energy_s22x5(sys3,correct_offset=False)
>>> get_interaction_energy_s22x5(sys1,dist=1.0)
>>> get_interaction_energy_s22x5(sys1,dist=0.9)
>>> get_interaction_energy_s22x5(sys1,dist=0.9,correct_offset=False)
>>> get_number_of_dimer_atoms(sys1)
[4, 4]
>>> get_s22x5_distance(sys2)
>>> get_s22x5_distance(sys3)

where sys1 is an s22 complex in the original geometry, while sys2 and sys3 are two different s22x5 geometries of the exact same complex. It is seen that the interaction energies for an s22 system and its s22x5 equivalent (indexed ‘_1.0’) does not necessarily match when the energy offset-correction is turned off. The last two functions are convenience functions, giving the number of atoms in the two molecules constituting a dimer and the relative intermolecular distance in a dimer (relative to the ‘1.0’ separation, and in Angstrom), respectively.