EnergySurfaceBase
class EnergySurfaceBase
Bases: abc.ABC
Class to hold a potential energy surface
Methods
eval
abstract EnergySurfaceBase.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
fit
abstract EnergySurfaceBase.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
get_equilibrium_geometry
abstract EnergySurfaceBase.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_minimal_energy
abstract EnergySurfaceBase.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_trust_region
abstract EnergySurfaceBase.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().
Return type
Tuple[float, float]
Returns
the trust region between bounds