qiskit.chemistry.algorithms.pes_samplers.EnergySurfaceBase¶

class EnergySurfaceBase[source]¶

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)[source]¶

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)[source]¶

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)[source]¶

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

abstract get_minimal_energy(scaling=1.0)[source]¶

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

abstract get_trust_region()[source]¶

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().

Return type: Tuple[float, float]
Returns: the trust region between bounds