The corresponding file can be obtained from:

• Jupyter Notebook: Getting_Started.ipynb

• Interactive Jupyter Notebook:

Getting Started

This tutorial shows how to use QC-AtomDB as a Python script or library.

Learning outcomes:

• Script usage

• Atom-DB as a Python library

  • Loading/Dumping atomic species data

  • Getting properties of atomic species

Requirements

This tutorial assumes the package has been installed and the datasets compiled. Please, check the pertinent instructions provided in the README file.

Script usage

A basic functionality is provided through the command line interface.

• Check the command line options with:

  • python -m atomdb -h

• To retrieve Slater’s dataset data for the neutral Carbon atom do:

  • python -m atomdb -q slater C 0 3

import sys
sys.path.insert(0, '../../')

Atom-DB as a Python library

The following lines should appear at the beginning of the scripts:

# Import the Atom-DB library
import atomdb

# Optional modules
import numpy as np

Loading/Dumping Data

Use the atomdb.load function to retrieve an atomic spcies’ information.

The required inputs are: element symbol, charge and multiplicity.

Optionally a dataset name can be specified choosing one of:

• nist: NIST spectroscopic data complemented with results from Phys. Chem. Chem. Phys., 2016,18, 25721-25734

• slater: Atomic orbitals and electronic denisty properties from SCF calculations using Slater basis.

• numeric: electronic denisty properties from numerical Hartree-fock calculations.

• gaussian: Atomic orbitals and electronic denisty properties from SCF calculations using gaussian basis.

Currently we only support neutral/charged elements in its ground state, therefore, the optional parameter nexc (electronic state number) is not specified, leaving it to take its dafault value 0.

# Define an atomic species and load its data
element = 'Cl'
charge = 0
mult = 2
dataset = 'slater'

atom = atomdb.load(element, charge, mult, dataset=dataset)

All information stored about a specie can be outputted in a JSON file format using:

# Dumping Data to a JSON string
atom.to_json()

Getting Atomic Properties

Bellow we showcase some of the accesible scalar and vector properties (mostly related to the electron density)

Note that we use dictionaries for properties like the non-bonded atomic radius having more than one definition.

• Scalars

print("Atomic number: ", atom.natom)
print("Number of electrons: ", atom.nelec)
print("Mass [a.u.]: ", atom.mass)
print("Atomic radius [a.u.]: ", atom.vdw_radii['bondi'])

Atomic number:  17
Number of electrons:  17
Mass [a.u.]:  35.4515
Atomic radius [a.u.]:  3.307020734362191

• Vectors

# Atomic orbitals
print("Number of spin-orbitals (alpha): ", atom.ao.norba)
print("Energies: ", atom.ao.energy_a)
print("Occupations (alpha): ", atom.ao.occs_a)
print("Occupations (beta): ", atom.ao.occs_b)

Number of spin-orbitals (alpha):  5
Energies:  [-104.8844208  -10.6074807   -1.0729121   -8.0722274   -0.5063999]
Occupations (alpha):  [1. 1. 1. 3. 3.]
Occupations (beta):  [1. 1. 1. 3. 2.]

• Functions (electron density properties)

Two methods are available to compute density-related properties: interpolate_dens and interpolate_ked; they return spline functions (cubic splines) that can be evaluated on a radial grid.

# Define a uniform radial grid and evaluate the density
rad_grid = np.linspace(0., 6., num=100)
dens_spline = atom.interpolate_dens(log=True)
ked_spline = atom.interpolate_ked(log=False)

dens = dens_spline(rad_grid)
ked = ked_spline(rad_grid)

import matplotlib.pyplot as plt

fig = plt.figure(figsize = (6, 6), dpi=100)
ax = fig.subplots()


ax.plot(rad_grid, dens, '-r', linewidth=2)
ax.set(xlabel="Radial distance [Bohr]", ylabel="Density [Bohr$^{-3}$]")
ax.set_yscale('log')
ax.set_ylim(top=1000, bottom=0.00001)
ax.set_xlim(left=0., right=6)
fig.suptitle(f'Spherically averaged density')
plt.show()