Featurizers

The Featurizer object allows for the featurization of systems into a format that can be fed into machine learning models. Specified within this object are all the desired settings for when featurizing systems. More specifically this includes:

  • featurizer_class: the desired class for featurization

  • preset: if the featurizer class can be instantiated by a preset, that preset can be specified here. (e.g. the magpie feature set for the ElementProperty featurizer class)

  • design_space_structures: if the design space is already known, the structures can be specified here to extract the max_size and species_list parameters. supercedes max_size and species_list upon instantiation

  • max_size: the largest structure size that the featurizer can encounter

  • species_list: all possible species that the featurizer can encounter

Applying the Featurizer there are two main methods: featurize_single and featurize_multiple. The former is intended for featurizing a single structure. On the other hand, the latter can take multiple structures and returns them in a single feature matrix.

Below are three examples using structure, site, and compositional featurization methods:

>>> from autocat.learning.featurizers import Featurizer
>>> from autocat.utils import flatten_structures_dict
>>> from autocat.surface import generate_surface_structures
>>> from dscribe.descriptors import SineMatrix
>>> surfs = flatten_structures_dict(generate_surface_structures(["Li", "Na"]))
>>> f = Featurizer(SineMatrix, design_space_structures=surfs)
>>> f
+-----------------------------------+-------------------------------------------+
|                                   |                 Featurizer                |
+-----------------------------------+-------------------------------------------+
|               class               | dscribe.descriptors.sinematrix.SineMatrix |
|               kwargs              |                    None                   |
|            species list           |                ['Na', 'Li']               |
|       maximum structure size      |                     36                    |
|               preset              |                    None                   |
| design space structures provided? |                    True                   |
+-----------------------------------+-------------------------------------------+
>>> X = f.featurize_multiple(surfs)

>>> from ase import Atoms
>>> from dscribe.descriptors import SOAP
>>> from autocat.learning.featurizers import Featurizer
>>> from autocat.utils import flatten_structures_dict
>>> from autocat.surface import generate_surface_structures
>>> from autocat.adsorption import place_adsorbate
>>> surf = flatten_structures_dict(generate_surface_structures(["Cu"]))[0]
>>> ads_struct = place_adsorbate(surf, Atoms("OH"))
>>> f = Featurizer(
...    SOAP,
...    max_size=36,
...    species_list=["Cu", "O", "H"],
...    kwargs={"rcut": 6., "lmax": 8, "nmax": 8}
... )
>>> f
+-----------------------------------+-------------------------------------+
|                                   |              Featurizer             |
+-----------------------------------+-------------------------------------+
|               class               |    dscribe.descriptors.soap.SOAP    |
|               kwargs              | {'rcut': 6.0, 'lmax': 8, 'nmax': 8} |
|            species list           |           ['Cu', 'O', 'H']          |
|       maximum structure size      |                  36                 |
|               preset              |                 None                |
| design space structures provided? |                False                |
+-----------------------------------+-------------------------------------+
>>> X = f.featurize_single(ads_struct)

>>> from autocat.learning.featurizers import Featurizer
>>> from autocat.utils import flatten_structures_dict
>>> from autocat.saa import generate_saa_structures
>>> from matminer.featurizers.composition import ElementProperty
>>> saas = flatten_structures_dict(generate_saa_structures(["Cu", "Au"],["Pt", "Pd"]))
>>> f = Featurizer(ElementProperty, preset="magpie", design_space_structures=saas)
>>> f
+-----------------------------------+------------------------------------------------------------+
|                                   |                         Featurizer                         |
+-----------------------------------+------------------------------------------------------------+
|               class               | matminer.featurizers.composition.composite.ElementProperty |
|               kwargs              |                            None                            |
|            species list           |                  ['Pt', 'Pd', 'Au', 'Cu']                  |
|       maximum structure size      |                             36                             |
|               preset              |                           magpie                           |
| design space structures provided? |                            True                            |
+-----------------------------------+------------------------------------------------------------+
>>> X = f.featurize_multiple(saas)

The goal of this Featurizer object is to provide a unified class across different featurization techniques.

At present the following featurizer classes are supported:

  • dscribe:

    • SineMatrix
    • CoulombMatrix
    • ACSF
    • SOAP

  • matminer:

    • ElementProperty
    • ChemicalSRO
    • OPSiteFingerprint
    • CrystalNNFingerprint

N.B. ACSF, SOAP, CrystalNNFingerprint, OPSiteFingerprint, and ChemicalSRO are all implemented to featurize locally around specified atoms indicated with ase.Atoms.tags <= 0. The remaining implemented featurizer classes consider the full structure by definition