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# Introduction: Nitrogen on copper

This section gives a quick (and incomplete) overview of what ASE can do.

We will calculate the adsorption energy of a nitrogen molecule on a copper surface. This is done by calculating the total energy for the isolated slab and for the isolated molecule. The adsorbate is then added to the slab and relaxed, and the total energy for this composite system is calculated. The adsorption energy is obtained as the sum of the isolated energies minus the energy of the composite system.

Here is a picture of the system after the relaxation:

Please have a look at the following script `N2Cu.py`:

    from ase import Atoms
    from ase.build import add_adsorbate, fcc111
    from ase.calculators.emt import EMT
    from ase.constraints import FixAtoms
    from ase.optimize import QuasiNewton

    h = 1.85
    d = 1.10

    slab = fcc111('Cu', size=(4, 4, 2), vacuum=10.0)

    slab.calc = EMT()
    e_slab = slab.get_potential_energy()

    molecule = Atoms('2N', positions=[(0., 0., 0.), (0., 0., d)])
    molecule.calc = EMT()
    e_N2 = molecule.get_potential_energy()

    add_adsorbate(slab, molecule, h, 'ontop')
    constraint = FixAtoms(mask=[a.symbol != 'N' for a in slab])
    slab.set_constraint(constraint)
    dyn = QuasiNewton(slab, trajectory='N2Cu.traj')
    dyn.run(fmax=0.05)

    print('Adsorption energy:', e_slab + e_N2 - slab.get_potential_energy())

Assuming you have ASE setup correctly (Installation) run the script:

    python N2Cu.py

Please read below what the script does.

## Atoms

The `Atoms` object is a collection of atoms. Here is how to define a N2 molecule by directly specifying the position of two nitrogen atoms:

    >>> from ase import Atoms
    >>> d = 1.10
    >>> molecule = Atoms('2N', positions=[(0., 0., 0.), (0., 0., d)])

You can also build crystals using, for example, the lattice module which returns `Atoms` objects corresponding to common crystal structures. Let us make a Cu (111) surface:

    >>> from ase.build import fcc111
    >>> slab = fcc111('Cu', size=(4,4,2), vacuum=10.0)

## Adding calculator

In this overview we use the effective medium theory (EMT) calculator, as it is very fast and hence useful for getting started.

We can attach a calculator to the previously created `Atoms` objects:

    >>> from ase.calculators.emt import EMT
    >>> slab.calc = EMT()
    >>> molecule.calc = EMT()

and use it to calculate the total energies for the systems by using the `get_potential_energy()` method from the `Atoms` class:

    >>> e_slab = slab.get_potential_energy()
    >>> e_N2 = molecule.get_potential_energy()

## Structure relaxation

Let’s use the `QuasiNewton` minimizer to optimize the structure of the N2 molecule adsorbed on the Cu surface. First add the adsorbate to the Cu slab, for example in the on-top position:

    >>> h = 1.85
    >>> add_adsorbate(slab, molecule, h, 'ontop')

In order to speed up the relaxation, let us keep the Cu atoms fixed in the slab by using `FixAtoms` from the `constraints` module. Only the N2 molecule is then allowed to relax to the equilibrium structure:

    >>> from ase.constraints import FixAtoms
    >>> constraint = FixAtoms(mask=[a.symbol != 'N' for a in slab])
    >>> slab.set_constraint(constraint)

Now attach the `QuasiNewton` minimizer to the system and save the trajectory file. Run the minimizer with the convergence criteria that the force on all atoms should be less than some `fmax`:

    >>> from ase.optimize import QuasiNewton
    >>> dyn = QuasiNewton(slab, trajectory='N2Cu.traj')
    >>> dyn.run(fmax=0.05)

Note

The general documentation on structure optimizations contains information about different algorithms, saving the state of an optimizer and other functionality which should be considered when performing expensive relaxations.

## Input-output

Writing the atomic positions to a file is done with the `write()` function:

    >>> from ase.io import write
    >>> write('slab.xyz', slab)

This will write a file in the xyz-format. Possible formats are:

format | description
--- | ---
`xyz` | Simple xyz-format
`cube` | Gaussian cube file
`pdb` | Protein data bank file
`traj` | ASE’s own trajectory format
`py` | Python script

Reading from a file is done like this:

    >>> from ase.io import read
    >>> slab_from_file = read('slab.xyz')

If the file contains several configurations, the default behavior of the `write()` function is to return the last configuration. However, we can load a specific configuration by doing:

    >>> read('slab.traj')      # last configuration
    >>> read('slab.traj', -1)  # same as above
    >>> read('slab.traj', 0)   # first configuration

## Visualization

The simplest way to visualize the atoms is the `view()` function:

    >>> from ase.visualize import view
    >>> view(slab)

This will pop up a `ase.gui` window. Alternative viewers can be used by specifying the optional keyword `viewer=...` - use one of ‘ase.gui’, ‘gopenmol’, ‘vmd’, or ‘rasmol’. (Note that these alternative viewers are not a part of ASE and will need to be installed by the user separately.) The VMD viewer can take an optional `data` argument to show 3D data:

    >>> view(slab, viewer='VMD', data=array)

### Molecular dynamics

Let us look at the nitrogen molecule as an example of molecular dynamics with the `VelocityVerlet` algorithm. We first create the `VelocityVerlet` object giving it the molecule and the time step for the integration of Newton’s law. We then perform the dynamics by calling its `run()` method and giving it the number of steps to take:

    >>> from ase.md.verlet import VelocityVerlet
    >>> from ase import units
    >>> dyn = VelocityVerlet(molecule, dt=1.0 * units.fs)
    >>> for i in range(10):
    ...     pot = molecule.get_potential_energy()
    ...     kin = molecule.get_kinetic_energy()
    ...     print('%2d: %.5f eV, %.5f eV, %.5f eV' % (i, pot + kin, pot, kin))
    ...     dyn.run(steps=20)