citrine.informatics.predictors.chemical_formula_featurizer module

class citrine.informatics.predictors.chemical_formula_featurizer.ChemicalFormulaFeaturizer(name: str, *, description: str, input_descriptor: ChemicalFormulaDescriptor, features: List[str] | None = None, excludes: List[str] | None = None, powers: List[int] | None = None)

Bases: Resource[ChemicalFormulaFeaturizer], PredictorNode

A featurizer for chemical formulae. Inspired by Magpie.

The ChemicalFormulaFeaturizer computes a configurable set of features on chemical formula data. The features are functions of element-level properties, which are inspired by Magpie. The features are configured using the features and excludes arguments, which accept either feature names or predefined aliases. Many features are stoichiometrically weighted generalized means of element-level properties. How to compute the mean is configured using the powers argument.

The default is the “standard” alias, corresponding to features that are intuitive and often correlate with properties of interest. Other aliases are “physical,” “electronic,” and “periodicTable.”

The following features are weighted means of simple elemental properties.

  • “Pauling electronegativity”: standard, electronic

  • “Number of d valence electrons”: standard, electronic

  • “Number of unfilled f valence electrons”: standard, electronic

  • “Number of f valence electrons”: standard, electronic

  • “Number of unfilled p valence electrons”: standard, electronic

  • “Number of p valence electrons”: standard, electronic

  • “Number of unfilled s valence electrons”: standard, electronic

  • “Number of s valence electrons”: standard, electronic

  • “Total number of unfilled valence electrons”: standard, electronic

  • “Total number of valence electrons”: standard, electronic

  • “Elemental work function”: standard, electronic

  • “Elemental polarizability”: standard, electronic

  • “Radius of d orbitals”: standard, electronic

  • “Radius of s orbitals”: standard, electronic

  • “Radius of p orbitals”: standard, electronic

  • “Elemental magnetic moment”: standard, electronic

  • “Elemental atomic volume”: standard, electronic, physical

  • “Elemental electron density”: standard, electronic

  • “Mendeleev number”: standard, periodicTable

  • “Row in periodic table”: standard, periodicTable

  • “Elemental bulk modulus”: standard, physical

  • “Elemental density”: standard, physical

  • “Elemental melting temperature”: standard, physical

  • “Elemental crystal structure (space group)”: standard, electronic, physical

  • “AtomicVolume”: electronic, physical

  • “Number”: periodicTable

  • “CovalentRadius”: electronic, physical

  • “DipolePolarizability”: electronic

  • “ElectronAffinity”: electronic

  • “FirstIonizationEnergy”: electronic

  • “GSbandgap”: electronic

  • “GSenergy_pa”: electronic

  • “GSestBCClatcnt”: electronic, physical

  • “GSvolume_pa”: electronic, physical

  • “MiracleRadius”: electronic, physical

  • “NdUnfilled”: electronic

  • “ZungerPP-r_pi”: electronic

  • “AtomicWeight”: physical, periodicTable

  • “Column in periodic table”: periodicTable

  • “IsAlkali”: periodicTable

  • “IsDBlock”: periodicTable

  • “IsFBlock”: periodicTable

  • “IsMetal”: periodicTable

  • “IsNonmetal”: periodicTable

  • “BoilingT”: physical

  • “FusionEnthalpy”: physical

  • “HeatCapacityMass”: physical

  • “HeatCapacityMolar”: physical

  • “HeatFusion”: physical

  • “ShearModulus”: physical

  • “ValenceZeff”: electronic, physical

The following features are weighted means of more complex elemental properties.

  • “Packing density”: standard, physical

  • “Liquid range”: standard, physical

  • “Non-dimensional liquid range”: standard, physical

  • “Liquid ratio”: standard, physical

  • “Elastic Poisson Ratio”: standard, physical

  • “DFT energy density”: standard, electronic, physical

  • “Interatomic distance”: standard, physical

  • “Ionization Affinity Ratio”: standard, electronic

  • “Ratio of Electron Affinity to Electronegativity”: standard, electronic

  • “Trouton’s Ratio”: standard, physical

  • “Miracle Ratio”: standard, electronic

  • “DFT volume ratio”: standard, physical

  • “Mulliken electronegativity”: standard, electronic

  • “Modulii sum”: standard, physical

  • “Zunger Pseudopotential radius ratio”: standard, electronic

  • “BCC Efficiency”: standard, physical

  • “Non-dimensional heat of fusion”: standard, physical

  • “Non-dimensional band gap”: standard, electronic

  • “Conduction ionization energy”: standard, electronic

  • “Valence electron density”: standard, electronic

  • “Non-dimensional work function”: standard, electronic

  • “Shear Modulus Melting Temp Product”: standard, physical

The following features are not weighted means. Their values do not depend on powers.

  • “Maximum electronegativity difference”: standard, electronic

  • “Maximum radius difference”: standard, electronic, physical

  • “Maximum radius ratio”: standard, electronic, physical

  • “Min atomic radius plus max electronegativity difference”: standard, electronic, physical

  • “Number of elements”

  • “Minimum atomic fraction”

  • “Maximum atomic fraction”

  • “Minimum weight fraction”: standard, periodicTable

  • “Maximum weight fraction”: standard, periodicTable

  • “Formula weight”: standard, physical

Parameters:

  • input_descriptor (ChemicalFormulaDescriptor) – the descriptor to featurize

  • features (Optional[List[str]]) – The list of features to compute, either by name or by group alias. Default is “standard.”

  • excludes (Optional[List[str]]) – The list of features to exclude, either by name or by group alias. Default is none. The final set of features generated by the predictor is set(features) - set(excludes).

  • powers (Optional[List[int]]) – The list of powers to use when computing generalized weighted means of element properties. p=1 corresponds to the ordinary mean, p=2 is the root mean square, etc.

access_control_dict() → dict

Return an access control entity representation of this resource. Internal use only.

classmethod build(data: dict) → Self

Build an instance of this object from given data.

dump() → dict

Dump this instance.

classmethod get_type(data) → Type[PredictorNode]

Return the subtype.

description = None
excludes = []
features = None
input_descriptor = None
name = None
powers = None
typ = 'ChemicalFormulaFeaturizer'