Parametrizations: Components

Elements

Parametrization: Element

Element parametrization record containing the adjustable parameters for each species.

class dxtb._src.param.element.Element(**data)

Bases: BaseModel

Representation of the parameters for a species.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• shells (List[str])

• levels (List[float])

• slater (List[float])

• ngauss (List[int])

• refocc (List[float])

• shpoly (List[float])

• kcn (List[float])

• gam (float)

• lgam (List[float])

• gam3 (float)

• zeff (float)

• arep (float)

• xbond (float)

• en (float)

• dkernel (float)

• qkernel (float)

• mprad (float)

• mpvcn (float)

arep: float

Repulsion exponent.

dkernel: float

Dipolar exchange-correlation kernel.

en: float

Electronegativity.

gam: float

Chemical hardness / Hubbard parameter.

gam3: float

Atomic Hubbard derivative.

kcn: List[float]

CN dependent shift of the self energy for each shell

levels: List[float]

Atomic level energies for each shell

lgam: List[float]

Relative chemical hardness for each shell.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

mprad: float

Offset radius for the damping in the AES energy.

mpvcn: float

Shift value in the damping in the AES energy. Only used if mprad != 0.

ngauss: List[int]

Number of primitive Gaussian functions used in the STO-NG expansion for each shell.

qkernel: float

Quadrupolar exchange-correlation kernel.

refocc: List[float]

Reference occupation for each shell

shells: List[str]

Included shells with principal quantum number and angular momentum.

shpoly: List[float]

Polynomial enhancement for Hamiltonian elements

slater: List[float]

Slater exponents of the STO-NG functions for each shell

xbond: float

Halogen bonding strength.

zeff: float

Effective nuclear charge used in repulsion.

Hamiltonian

Parametrization: Hamiltonian

Definition of the global core Hamiltonian parameters.

The core Hamiltonian is rescaling the shell-blocks of the overlap integrals formed over the basis set by the average of the atomic self-energies and an additional distance dependent function formed from the element parametrization.

class dxtb._src.param.hamiltonian.Hamiltonian(**data)

Bases: BaseModel

Possible Hamiltonian parametrizations. Currently only the xTB Hamiltonian is supported.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

xtb (XTBHamiltonian)

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

xtb: XTBHamiltonian

Data for the xTB Hamiltonian

class dxtb._src.param.hamiltonian.XTBHamiltonian(**data)

Bases: BaseModel

Global parameters for the formation of the core Hamiltonian from the overlap integrals. Contains the required atomic and shell dependent scaling parameters to obtain the off-site scaling functions independent of the self-energy and the distance polynomial.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• wexp (float)

• kpol (float)

• enscale (float)

• cn (str | None)

• shell (Dict[str, float])

• kpair (Dict[str, float])

cn: Optional[str]

Local environment descriptor for shifting the atomic self-energies

enscale: float

Electronegativity scaling factor for off-site valence blocks

kpair: Dict[str, float]

Atom-pair dependent scaling factor for off-site valence blocks

kpol: float

Scaling factor for polarization functions

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shell: Dict[str, float]

Shell-pair dependent scaling factor for off-site blocks

wexp: float

Exponent of the orbital exponent dependent off-site scaling factor

Interactions

Parametrization: Electrostatics (2nd order)

Definition of the isotropic second-order charge interactions.

class dxtb._src.param.charge.Charge(**data)

Bases: BaseModel

Possible charge parametrizations. Currently only the interaction kernel for the Klopman-Ohno electrostatics (effective) is supported.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

effective (ChargeEffective)

effective: ChargeEffective

Klopman-Ohno electrostatics.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dxtb._src.param.charge.ChargeEffective(**data)

Bases: BaseModel

Representation of the isotropic second-order charge interactions for a parametrization.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• gexp (float)

• average (str)

average: str

Averaging function for Hubbard parameter.

gexp: float

Exponent of Coulomb kernel.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Parametrization: Electrostatics (3rd order)

Definition of the isotropic third-order onsite correction.

class dxtb._src.param.thirdorder.ThirdOrder(**data)

Bases: BaseModel

Representation of the isotropic third-order onsite correction.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

shell (Literal[False] | ~dxtb._src.param.thirdorder.ThirdOrderShell)

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shell: Union[Literal[False], ThirdOrderShell]

Whether the third order contribution is shell-dependent or only atomwise.

class dxtb._src.param.thirdorder.ThirdOrderShell(**data)

Bases: BaseModel

Representation of shell-resolved third-order electrostatics.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• s (float)

• p (float)

• d (float)

d: float

Scaling factor for d-orbitals.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

p: float

Scaling factor for p-orbitals.

s: float

Scaling factor for s-orbitals.

Classical

Parametrization: Dispersion

Definitions of dispersion contributions. Contains the D3Model and D4Model representing the DFT-D3(BJ) and DFT-D4 dispersion corrections, respectively. For details on there implementation, see the tad-dftd3 and tad-dftd4 libraries.

class dxtb._src.param.dispersion.D3Model(**data)

Bases: BaseModel

Representation of the DFT-D3(BJ) contribution for a parametrization.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• s6 (float)

• s8 (float)

• a1 (float)

• a2 (float)

• s9 (float)

a1: float

Becke-Johnson damping parameter.

a2: float

Becke-Johnson damping parameter.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s6: float

Scaling factor for multipolar (dipole-dipole contribution) terms.

s8: float

Scaling factor for multipolar (dipole-quadrupole contribution) terms.

s9: float

Scaling factor for the many-body dispersion term (ATM/RPA-like).

class dxtb._src.param.dispersion.D4Model(**data)

Bases: BaseModel

Representation of the DFT-D4 contribution for a parametrization.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• sc (bool)

• s6 (float)

• s8 (float)

• a1 (float)

• a2 (float)

• s9 (float)

• s10 (float)

• alp (float)

a1: float

Becke-Johnson damping parameter.

a2: float

Becke-Johnson damping parameter.

alp: float

Exponent of zero damping function in the ATM term.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

s10: float

Scaling factor for quadrupole-quadrupole term.

s6: float

Scaling factor for multipolar (dipole-dipole contribution) terms

s8: float

Scaling factor for multipolar (dipole-quadrupole contribution) terms

s9: float

Scaling factor for the many-body dispersion term (ATM/RPA-like).

sc: bool

Whether the dispersion correctio is used self-consistently or not.

class dxtb._src.param.dispersion.Dispersion(**data)

Bases: BaseModel

Possible dispersion parametrizations. Currently, the DFT-D3(BJ) and DFT-D4 methods are supported.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• d3 (D3Model | None)

• d4 (D4Model | None)

d3: Optional[D3Model]

D3 model for the dispersion.

d4: Optional[D4Model]

D4 model for the dispersion.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Parametrization: Halogen

Definitions for halogen binding corrections. Currently, only GFN1-xTB’s classical halogen bond correction is defined.

class dxtb._src.param.halogen.ClassicalHalogen(**data)

Bases: BaseModel

Representation of the classical geometry dependent halogen-bond (XB) correction for a parametrization.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• damping (float)

• rscale (float)

damping: float

Damping factor of attractive contribution in Lennard-Jones-like potential.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

rscale: float

Global scaling factor for covalent radii of AX bond.

class dxtb._src.param.halogen.Halogen(**data)

Bases: BaseModel

Possible halogen correction parametrizations.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

classical (ClassicalHalogen)

classical: ClassicalHalogen

Classical halogen-bond correction used in GFN1-xTB.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Parametrization: Repulsion

Definition of the repulsion contribution. The EffectiveRepulsion is used in GFN1-xTB and GFN2-xTB.

class dxtb._src.param.repulsion.EffectiveRepulsion(**data)

Bases: BaseModel

Representation of the repulsion contribution for a parametrization.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• kexp (float)

• klight (float | None)

kexp: float

Scaling of the interatomic distance in the exponential damping function of the repulsion energy.

klight: Optional[float]

Scaling of the interatomic distance in the exponential damping function of the repulsion energy for light elements, i.e., H and He (only GFN2).

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dxtb._src.param.repulsion.Repulsion(**data)

Bases: BaseModel

Possible repulsion parametrizations. Currently only the GFN1-xTB effective repulsion is supported.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

effective (EffectiveRepulsion)

effective: EffectiveRepulsion

Name of the represented method

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Misc

Parametrizations: Meta

Meta data associated with a parametrization. Mainly used for identification of data format.

class dxtb._src.param.meta.Meta(**data)

Bases: BaseModel

Representation of the meta data for a parametrization.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

• name (str | None)

• reference (str | None)

• version (int)

• format (int | None)

format: Optional[int]

Format version of the parametrization data.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: Optional[str]

Name of the represented method.

reference: Optional[str]

References relevant for the parametrization records.

version: int

Version of the represented method.