qiskit.chemistry.algorithms.pes_samplers.EnergySurfaceBase

class EnergySurfaceBase

Class to hold a potential energy surface

__init__()

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__()	Initialize self.
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_minimal_energy([scaling])	Get the minimal energy.
get_trust_region()	Get the trust region.

abstract eval(x)

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

パラメータ

x (float) – value to evaluate surface in

戻り値の型

float

戻り値

value of surface in point x

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

Fits surface to data

パラメータ

• 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)

戻り値の型

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.

パラメータ

scaling (float) – scaling factor

戻り値の型

float

戻り値

equilibrium geometry

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.

パラメータ

scaling (float) – scaling factor

戻り値の型

float

戻り値

minimum energy

abstract get_trust_region()

Get the trust region.

Returns the bounds of the region (in space) where the energy surface implementation can be trusted. When doing spline interpolation, for example, that would be the region where data is interpolated (vs. extrapolated) from the arguments of fit().

戻り値の型

Tuple[float, float]

戻り値

the trust region between bounds