How to use atoml - 4 common examples

# Usually, the chemical formula (Hill) is available for the adsorbate.
    # It will be used by AtoML if attached like so:
    slab.info['key_value_pairs'] = {}
    slab.info['key_value_pairs']['species'] = ads
    # If the adsorbate consist of the same elements that the slab does,
    # it is preferable to specify the atomic indices belonging to the adsorbate.
    slab.info['ads_atoms'] = [12]
    # Append them to a list.
    images.append(slab)

# If you import from an ase.db, it is recommended to make use of
    # atoml.api.ase_atoms_api.database_to_list

# Some information is expected to be attached to atoms objects.
#   There are various ways of doing this, but the easiest is to call
AtoML_atoms = autogen_info(images)
# This is where checks should be made

# Instantiate the fingerprint generator for adsorbate structures.
fingerprinter = FeatureGenerator()

# All user methods under the fingerprinter accepts an atoms object and
#   returns a vector.
functions = [fingerprinter.mean_chemisorbed_atoms,
             fingerprinter.count_chemisorbed_fragment,
             fingerprinter.count_ads_atoms,
             fingerprinter.count_ads_bonds,
             fingerprinter.ads_av,
             fingerprinter.ads_sum,
             fingerprinter.bulk,
             fingerprinter.term,
             fingerprinter.strain,

Gen = SlabGenerator(
    atoms,
    miller_index=[1, 1, 1],
    layers=3,
    fixed=1,
    vacuum=8)

terminations = Gen.get_unique_terminations()
# Loop over unique surface terminations.
images = []
for i, t in enumerate(terminations):
    # Get the slab.
    slab = Gen.get_slab(iterm=i)
    slab.center(axis=2, vacuum=5)
    # Add an adsorbate to each slab.
    radii = np.average([get_radius(a.number) for a in slab])
    height = radii + covalent_radii[6]
    add_adsorbate(slab, ads, height, position=slab.get_positions()[-1, :2])
    # Usually, the chemical formula (Hill) is available for the adsorbate.
    # It will be used by AtoML if attached like so:
    slab.info['key_value_pairs'] = {}
    slab.info['key_value_pairs']['species'] = ads
    # If the adsorbate consist of the same elements that the slab does,
    # it is preferable to specify the atomic indices belonging to the adsorbate.
    slab.info['ads_atoms'] = [12]
    # Append them to a list.
    images.append(slab)

# If you import from an ase.db, it is recommended to make use of
    # atoml.api.ase_atoms_api.database_to_list

# Some information is expected to be attached to atoms objects.

# If the adsorbate consist of the same elements that the slab does,
    # it is preferable to specify the atomic indices belonging to the adsorbate.
    slab.info['ads_atoms'] = [12]
    # Append them to a list.
    images.append(slab)

# If you import from an ase.db, it is recommended to make use of
    # atoml.api.ase_atoms_api.database_to_list

# Some information is expected to be attached to atoms objects.
#   There are various ways of doing this, but the easiest is to call
AtoML_atoms = autogen_info(images)
# This is where checks should be made

# Instantiate the fingerprint generator for adsorbate structures.
fingerprinter = FeatureGenerator()

# All user methods under the fingerprinter accepts an atoms object and
#   returns a vector.
functions = [fingerprinter.mean_chemisorbed_atoms,
             fingerprinter.count_chemisorbed_fragment,
             fingerprinter.count_ads_atoms,
             fingerprinter.count_ads_bonds,
             fingerprinter.ads_av,
             fingerprinter.ads_sum,
             fingerprinter.bulk,
             fingerprinter.term,
             fingerprinter.strain,
             fingerprinter.mean_surf_ligands,
             fingerprinter.mean_site,
             fingerprinter.sum_site,
             fingerprinter.dbid,

# This list is passed on to the following setup functions,
#    along with a list of atoms.

# Get and print the names of features.
features_labels = fingerprinter.return_names(functions)
for l in range(len(features_labels)):
    print(l, features_labels[l])

# Get a matrix containing the fingerprints.
unlabeled_data_matrix = fingerprinter.return_vec(AtoML_atoms, functions)
print(np.shape(unlabeled_data_matrix), 'data matrix created.')

# Cleanup in case some of the functions are returning NaNs or Infs
print("Cleaning data.")
clean_data_matrix = clean_infinite(unlabeled_data_matrix)['train']

# Ready for Machine learning.
print(np.shape(clean_data_matrix), 'data matrix returned.')