MullerBrown

List of Methods

class taps.models.mullerbrown.MullerBrown(results=None, label=None, prj=None, _cache=None, unit='eV')

Muller Brown Potential

\[\begin{equation} V\left(x,y\right) = \sum_{\mu=1}^{4}{A_\mu e^{a_\mu \left(x-x_\mu^0\right)^2 + b_\mu \left(x-x_\mu^0\right) \left(y-y_\mu^0\right) + c_\mu\left(y-y_\mu^0\right)^2}} \end{equation}\]

• Initial position = (-0.55822365, 1.44172582)

• Final position = (0.6234994, 0.02803776)

Parameters:

• np.array([-200 (A =) –

• -100 –

• -170 –

• 15]) –

• np.array([-1 (a =) –

• -1 –

• -6.5 –

• 0.7]) –

• np.array([0 (y0 =) –

• 0 –

• 11 –

• 0.6]) –

• np.array([-10 (c =) –

• -10 –

• -6.5 –

• 0.7]) –

• np.array([1 (x0 =) –

• 0 –

• -0.5 –

• -1]) –

• np.array([0 –

• 0.5 –

• 1.5 –

• 1]) –

• 'unitless' (potential_unit =) –

Example

>>> import numpy as np
>>> N = 300
>>> x = np.linspace(-0.55822365, 0.6234994, N)
>>> y = np.linspace(1.44172582, 0.02803776, N)
>>> paths.coords = np.array([x, y])

Methods

__call__(coords)	Call self as a function.
calculate(coords[, properties])	x : N shape array y : N shape array return D x N
generate_unique_hash(positions)	return string that explains current calculation
get_properties([paths, properties, index, ...])	pre-calculation rutine.

get_covariance
get_directory
get_finite_gradients
get_finite_hessian
get_forces
get_gradient
get_gradients
get_hessian
get_label
get_potential
get_potential_energies
get_potential_energy
get_potentials
get_state_info
save

calculate(coords, properties=['potential'], **kwargs)

x : N shape array y : N shape array return D x N