qiskit.chemistry.algorithms.pes_samplers.PotentialBase¶

class PotentialBase(molecule)[source]¶

Class to hold prescribed 1D potentials (e.g. Morse/Harmonic) over a degree of freedom.

__init__(molecule)¶: Initialize self. See help(type(self)) for accurate signature.

Methods

`__init__`(molecule)	Initialize self.
`dissociation_energy`([scaling])	Returns the dissociation energy (scaled by ‘scaling’)
`eval`(x)	After fitting the data to the fit function, predict the energy at a point x.
`fit`(xdata, ydata[, initial_vals, bounds_list])	Fits surface to data
`get_equilibrium_geometry`([scaling])	Get the equilibrium energy.
`get_maximum_trusted_level`([n])	Returns the maximum energy level for which the particular implementation still provides a good approximation of reality.
`get_minimal_energy`([scaling])	Get the minimal energy.
`get_num_modes`()	This (1D) potential represents a single vibrational mode
`get_trust_region`()	The potential will usually be well-defined (even if not useful) for arbitrary x so we return a fairly large interval here.
`update_molecule`(molecule)	Wipe state if molecule changes, and check validity of molecule for potential.
`vibrational_energy_level`(n)	Returns the n-th vibrational energy level for a given mode.

abstract dissociation_energy(scaling=1.0)[source]¶

Returns the dissociation energy (scaled by ‘scaling’)

Return type: float

abstract eval(x)¶

After fitting the data to the fit function, predict the energy at a point x.

Parameters: x (float) – value to evaluate surface in
Return type: float
Returns: value of surface in point x

abstract fit(xdata, ydata, initial_vals=None, bounds_list=None)¶

Fits surface to data

Parameters

xdata (List[float]) – x data to be fitted
ydata (List[float]) – y data to be fitted
initial_vals (Optional[List[float]]) – Initial values for fit parameters. None for default. Order of parameters is d_e, alpha, r_0 and m_shift (see fit_function implementation)
bounds_list (Optional[Tuple[List[float], List[float]]]) – Bounds for the fit parameters. None for default. Order of parameters is d_e, alpha, r_0 and m_shift (see fit_function implementation)

Return type

None

abstract get_equilibrium_geometry(scaling=1.0)¶

Get the equilibrium energy.

Returns the geometry for the minimal energy (scaled by ‘scaling’) Default units (scaling=1.0) are Angstroms. Scale by 1E-10 to get meters.

Parameters: scaling (float) – scaling factor
Return type: float
Returns: equilibrium geometry

get_maximum_trusted_level(n=0)¶

Returns the maximum energy level for which the particular implementation still provides a good approximation of reality. Default value of 100. Redefined where needed (see e.g. Morse).

Parameters: n (int) – vibronic mode
Return type: float
Returns: maximum_trusted_level setted

abstract get_minimal_energy(scaling=1.0)¶

Get the minimal energy.

Returns the value of the minimal energy (scaled by ‘scaling’) Default units (scaling=1.0) are J/mol. Scale appropriately for Hartrees.

Parameters: scaling (float) – scaling factor
Return type: float
Returns: minimum energy

get_num_modes()[source]¶

This (1D) potential represents a single vibrational mode

Return type: int

get_trust_region()[source]¶

The potential will usually be well-defined (even if not useful) for arbitrary x so we return a fairly large interval here. Redefine in derived classes if needed.

Return type: Tuple[float, float]

update_molecule(molecule)¶

Wipe state if molecule changes, and check validity of molecule for potential.

Parameters: molecule (Molecule) – chemistry molecule
Return type: Molecule
Returns: molecule used

abstract vibrational_energy_level(n)¶

Returns the n-th vibrational energy level for a given mode.

Parameters: n (int) – number of vibrational mode
Return type: float
Returns: n-th vibrational energy level for a given mode