bdld.potential package

Submodules

bdld.potential.mueller_brown module

Müller-Brown Potential class to be evaluated with md

class bdld.potential.mueller_brown.MuellerBrownPotential(scaling_factor=None)

Bases: Potential

Müller-Brown potential

This is a 2D potential with three metastable states

It consists of a sum of four exponential terms: -200*exp( -(x-1)^2 -10*y^2 ) -100*exp( -x^2 -10*(y-0.5)^2 ) -170*exp( -6.5*(x+0.5)^2 + 11*(x+0.5)*(y-1.5) -6.5*(y-1.5)^2 ) +15*exp( 0.7*(x+1)^2 +0.6*(x+1)*(y-1) +0.7*(y-1)^2 )

The potential and its coefficients are described in K. Müller and L. D. Brown, Theoretical Chemistry Accounts, 53, 1979 pp. 75–93.

Parameters:

scaling_factor (Optional[float]) – scale potential by factor

energy(pos)

Get energy at position

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated (given as list or array even in 1d)

Return type:

float

Returns:

energy

evaluate(pos)

Get potential energy and force at position

Faster than base method because exponentials need to be evaluated only once

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated

Return type:

Tuple[float, ndarray]

Returns:

(energy, force)

force(pos)

Get force at position

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated (given as list or array even in 1d)

Return force:

array with force per direction

Return type:

ndarray

bdld.potential.polynomial module

Potential based on polynomial functions to be evaluated with md

class bdld.potential.polynomial.PolynomialPotential(coeffs, ranges=None)

Bases: Potential

Simple polynomial potential class defined by coefficients

Parameters:

  • coeffs (Union[List[float], ndarray]) – Coefficients of polynomial potential

  • der – Coefficients of derivative of potential per direction

  • n_dim – Dimensions of potential

  • polyval – polyval function of np.polynomial.polynomial to use

  • ranges (Optional[List[Tuple[float, float]]]) – (min, max) values of potential (optional)

energy(pos)

Get energy at position

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated (given as list or array even in 1d)

Return energy:

energy at position

Return type:

float

force(pos)

Get force at position

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated (given as list or array even in 1d)

Return force:

array with force per direction

Return type:

ndarray

set_polyval()

Selects polyval function from numpy.polynomial.polynomial depending on self.n_dim

Return type:

Callable

bdld.potential.polynomial.coefficients_from_file(filename, n_dim)

Read coefficients from file

This should be compatible to the ves_md_linearexpansion files

The syntax for the files is rows with one coefficient each. The first n_dim columns contain the polynomial orders per dimension, the next one the coefficient value. Remaining columns are ignored.

Parameters:

  • filename (str) – path to file holding the coefficients

  • n_dim (int) – dimensions of potential

Return type:

ndarray

bdld.potential.potential module

Potential class to be evaluated with md

class bdld.potential.potential.BoundaryCondition(value)

Bases: Enum

Enum for the different boundary conditions

periodic = 2
reflective = 1
class bdld.potential.potential.Potential

Bases: object

Base class for potentials

Collection of some functionality that all should have All derived potentials must implement energy() and force() functions and set some description in __str__()

apply_boundary_condition_periodic(pos, mom)

Apply periodic boundary condition

If the particle is outside the potential range, it is moved to the other side of the potential range Because position and momentum are numpy arrays, they are passed by reference and can be changed in place

Parameters:

  • pos (ndarray) – position of particle per direction

  • mom (ndarray) – momentum of particle per direction (not actually changed)

Return type:

None

apply_boundary_condition_reflective(pos, mom)

Apply reflective boundary condition

If the particle is outside the potential range, it is set to the boundary and its momentum is reversed

Because position and momentum are numpy arrays, they are passed by reference and can be changed in place

Parameters:

  • pos (ndarray) – position of particle per direction

  • mom (ndarray) – momentum of particle per direction

Return type:

None

property boundary_condition

Type of boundary condition for the potential

calculate_probability_density(kt, ranges, grid_points)

Calculate the probability density associated with the potential on a grid

Parameters:

  • kt (float) – Thermal energy of system

  • ranges (List[Tuple[float, float]]) – List of ranges of the grid per dimension (min, max)

  • grid_points (List[int]) – number of points per dimension

Raises:

ValueError – if dimensions of points or ranges and potential do not match

Return prob:

grid with normalized probablities

Return type:

Grid

calculate_reference(pos, mintozero=True)

Calculate reference from potential at given positions

Parameters:

  • pos (Union[List[ndarray], ndarray]) – positions to evaluate

  • mintozero (bool) – shift fes minimum to zero

Return fes:

list numpy array with fes values at positions

Return type:

ndarray

energy(pos)

Get energy at position, needs to be overriden by derived class

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated

Return type:

float

Returns:

energy

evaluate(pos)

Get potential energy and forces at position

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated

Return type:

Tuple[float, ndarray]

Returns:

(energy, forces)

force(pos)

Get energy at position, needs to be overriden by derived class

Parameters:

pos (Union[List[float], ndarray]) – position to be evaluated

Return type:

ndarray

Returns:

array with force per direction

get_fields()

Return list of identifiers for the potential dimensions

Can be overwritten by subclasses to have custom names

Return type:

List[str]

Module contents

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