# md.pair.aniso

Overview

 ALJ Anistropic LJ potential. AnisotropicPair Generic anisotropic pair potential. Dipole Screened dipole-dipole interactions. GayBerne Gay-Berne anisotropic pair potential.

Details

Anisotropic potentials.

class hoomd.md.pair.aniso.ALJ(nlist, default_r_cut=None, mode='none')

Anistropic LJ potential.

Parameters

ALJ computes the Lennard-Jones potential between anisotropic particles as described in Ramasubramani, V. et. al. 2020. Specifically we implement the formula:

\begin{eqnarray*} V_{\mathrm{ALJ}}(r, r_c) = & 4 \varepsilon \left[ \left( \frac{\sigma}{r} \right)^{12} - \left( \frac{\sigma}{r} \right)^{6} \right] + 4 \varepsilon_c \left[ \left( \frac{\sigma_c}{r_c} \right)^{12} - \left( \frac{\sigma_c}{r_c} \right)^{6} \right] \\ \end{eqnarray*}

The first term is the standard center-center interaction between two Lennard-Jones spheres. The second term is a contact interaction computed based on the smallest distance between the surfaces of the two shapes, $$r_c$$. The total potential energy can thus be viewed as the sum of two interactions, a central Lennard-Jones potential and a shifted Lennard-Jones potential where the shift is anisotroipc and depends on the extent of the shape in each direction.

Like a standard LJ potential, each term has an independent cutoff beyond which it decays to zero the behavior of these cutoffs is dependendent on whether a user requires LJ or Weeks-Chandler-Anderson (WCA)-like (repulsive-only) behavior. This behavior is controlled using the alpha parameter, which can take on the following values:

• 0: All interactions are WCA (no attraction).

• 1: Center-center interactions include attraction, contact-contact interactions are solely repulsive.

• 2: Center-center interactions are solely repulsive, contact-contact interactions include attraction.

• 3: All interactions include attractive and repulsive components.

For polytopes, computing interactions using a single contact point leads to significant instabilities in the torques because the contact point can jump from one end of a face to another in an arbitrarily small time interval. To ameliorate this, the ALJ potential performs a local averaging over all the features associated with the closest simplices on two polytopes. This averaging can be turned off by setting the average_simplices key for the type pair to False.

params

The ALJ potential parameters. The dictionary has the following keys:

• epsilon (float, required) - base energy scale $$\varepsilon$$ $$[energy]$$.

• sigma_i (float, required) - the insphere radius of the first particle type, $$[length]$$.

• sigma_j (float, required) - the insphere radius of the second particle type, $$[length]$$.

• alpha (int, required) - Integer 0-3 indicating whether or not to include the attractive component of the interaction (see above for details).

• contact_ratio_i (float, optional) - the ratio of the contact sphere radius of the first type with sigma_i. Defaults to 0.15.

• contact_ratio_j (float, optional) - the ratio of the contact sphere radius of the second type with sigma_j. Defaults to 0.15.

• average_simplices (bool, optional) - Whether to average over simplices. Defaults to True. See class documentation for more information.

Type: hoomd.data.typeparam.TypeParameter [tuple [particle_types, particle_types], dict]

shape

The shape of a given type. The dictionary has the following keys per type:

Type: hoomd.data.typeparam.TypeParameter [particle_types, dict]

Specifying only rounding_radii creates an ellipsoid, while specifying only vertices creates a convex polytope (set vertices and faces to empty list to create the ellipsoid). To automate the computation of faces, the convenience class method get_ordered_vertices can be used. However, because merging of faces requires applying a numerical threshold to find coplanar faces, in some cases get_ordered_vertices may result in not all coplanar faces actually being merged. In such cases, users can precompute the faces and provide them.

Example:

nl = hoomd.md.nlist.Cell()
alj = hoomd.md.pair.aniso.ALJ(nl, r_cut=2.5)

cube_verts = [(-0.5, -0.5, -0.5),
(-0.5, -0.5, 0.5),
(-0.5, 0.5, -0.5),
(-0.5, 0.5, 0.5),
(0.5, -0.5, -0.5),
(0.5, -0.5, 0.5),
(0.5, 0.5, -0.5),
(0.5, 0.5, 0.5)];

cube_faces = [[0, 2, 6],
[6, 4, 0],
[5, 0, 4],
[5,1,0],
[5,4,6],
[5,6,7],
[3,2,0],
[3,0,1],
[3,6,2],
[3,7,6],
[3,1,5],
[3,5,7]]

alj.params[("A", "A")] = dict(epsilon=2.0,
sigma_i=1.0,
sigma_j=1.0,
alpha=1,
)
alj.shape["A"] = dict(vertices=cube_verts,
faces=cube_faces,


Warning

Changing dimension in a simulation will invalidate this force and will lead to error or unrealistic behavior.

static get_ordered_vertices(vertices, return_faces=True)

Compute vertices and faces of a convex hull of given vertices.

Warning

This method requires the coxeter package.

Parameters
• vertices ($$(N_v, 3)$$ numpy.ndarray of float) – The vertices to take the convex hull of and get ordered vertices and faces from.

• return_faces (bool, optional) – Whether to return faces as a list of list of int which index into the returned vertices. Defaults to True. If False only vertices are returned and the return type is not a tuple.

Returns

A tuple containing:

Return type

tuple

property type_shapes

The shape specification for use with GSD files for visualization.

This is not meant to be used for access to shape information in Python. See the attribute shape for programatic assess. Use this property to log shape for visualization and storage through the GSD file type.

(Loggable: category=”object”)

Type

list [dict [str, any]]

class hoomd.md.pair.aniso.AnisotropicPair(nlist, default_r_cut=None, mode='none')

Generic anisotropic pair potential.

Users should not instantiate AnisotropicPair directly. It is a base class that provides common features to all anisotropic pair forces. All anisotropic pair potential commands specify that a given potential energy, force and torque be computed on all non-excluded particle pairs in the system within a short range cutoff distance $$r_{\mathrm{cut}}$$. The interaction energy, forces and torque depend on the inter-particle separation $$\vec r$$ and on the orientations $$\vec q_i$$, $$q_j$$, of the particles.

AnisotropicPair is similar to hoomd.md.pair.Pair except it does not support the 'xplor' shifting mode or r_on.

Parameters
• nlist (hoomd.md.nlist.NList) – The neighbor list.

• default_r_cut (float, optional) – The default cutoff for the potential, defaults to None which means no cutoff $$[\mathrm{length}]$$.

• mode (str, optional) – the energy shifting mode, defaults to “none”.

class hoomd.md.pair.aniso.Dipole(nlist, default_r_cut=None, mode='none')

Screened dipole-dipole interactions.

Implements the force and energy calculations for both magnetic and electronic dipole-dipole interactions. When particles have charge as well as a dipole moment, the interactions are through electronic dipole moments. If the particles have no charge then the interaction is through magnetic or electronic dipoles. Note whether a dipole is magnetic or electronic does not change the functional form of the potential only the units associated with the potential parameters.

Parameters

Dipole computes the (screened) interaction between pairs of particles with dipoles and electrostatic charges. The total energy computed is:

\begin{align}\begin{aligned}U_{dipole} = U_{dd} + U_{de} + U_{ee}\\U_{dd} = A e^{-\kappa r} \left(\frac{\vec{\mu_i}\cdot\vec{\mu_j}}{r^3} - 3\frac{(\vec{\mu_i}\cdot \vec{r_{ji}}) (\vec{\mu_j}\cdot \vec{r_{ji}})} {r^5} \right)\\U_{de} = A e^{-\kappa r} \left(\frac{(\vec{\mu_j}\cdot \vec{r_{ji}})q_i}{r^3} - \frac{(\vec{\mu_i}\cdot \vec{r_{ji}})q_j}{r^3} \right)\\U_{ee} = A e^{-\kappa r} \frac{q_i q_j}{r}\end{aligned}\end{align}

See hoomd.md.pair.Pair for details on how forces are calculated and the available energy shifting and smoothing modes. Use params dictionary to set potential coefficients. The coefficients must be set per unique pair of particle types.

Note

All units are given for electronic dipole moments.

params

The dipole potential parameters. The dictionary has the following keys:

• A (float, required) - $$A$$ - electrostatic energy scale (default: 1.0) $$[\mathrm{energy} \cdot \mathrm{length} \cdot \mathrm{charge}^{-2}]$$

• kappa (float, required) - $$\kappa$$ - inverse screening length $$[\mathrm{length}^{-1}]$$

Type: TypeParameter [tuple [particle_type, particle_type], dict]

mu

$$\mu$$ - the magnetic magnitude of the particle local reference frame as a tuple (i.e. $$(\mu_x, \mu_y, \mu_z)$$) $$[\mathrm{charge} \cdot \mathrm{length}]$$.

Type: TypeParameter [particle_type, tuple [float, float, float ]]

Example:

nl = nlist.Cell()
dipole = md.pair.Dipole(nl, default_r_cut=3.0)
dipole.params[('A', 'B')] = dict(A=1.0, kappa=4.0)
dipole.mu['A'] = (4.0, 1.0, 0.0)

class hoomd.md.pair.aniso.GayBerne(nlist, default_r_cut=None, mode='none')

Gay-Berne anisotropic pair potential.

Warning: The code has yet to be updated to the current API.

Parameters

GayBerne computes the Gay-Berne potential between anisotropic particles.

This version of the Gay-Berne potential supports identical pairs of uniaxial ellipsoids, with orientation-independent energy-well depth. The potential comes from the following paper Allen et. al. 2006 paper link.

The interaction energy for this anisotropic pair potential is

\begin{eqnarray*} V_{\mathrm{GB}}(\vec r, \vec e_i, \vec e_j) = & 4 \varepsilon \left[ \zeta^{-12} - \zeta^{-6} \right]; & \zeta < \zeta_{\mathrm{cut}} \\ = & 0; & \zeta \ge \zeta_{\mathrm{cut}} \\ \end{eqnarray*}
\begin{align}\begin{aligned}\zeta = \left(\frac{r-\sigma+\sigma_{\mathrm{min}}} {\sigma_{\mathrm{min}}}\right)\\\sigma^{-2} = \frac{1}{2} \hat{\vec{r}} \cdot \vec{H^{-1}} \cdot \hat{\vec{r}}\\\vec{H} = 2 \ell_\perp^2 \vec{1} + (\ell_\parallel^2 - \ell_\perp^2) (\vec{e_i} \otimes \vec{e_i} + \vec{e_j} \otimes \vec{e_j})\end{aligned}\end{align}

with $$\sigma_{\mathrm{min}} = 2 \min(\ell_\perp, \ell_\parallel)$$.

The cut-off parameter $$r_{\mathrm{cut}}$$ is defined for two particles oriented parallel along the long axis, i.e. $$\zeta_{\mathrm{cut}} = \left(\frac{r-\sigma_{\mathrm{max}} + \sigma_{\mathrm{min}}}{\sigma_{\mathrm{min}}}\right)$$ where $$\sigma_{\mathrm{max}} = 2 \max(\ell_\perp, \ell_\parallel)$$ .

The quantities $$\ell_\parallel$$ and $$\ell_\perp$$ denote the semi-axis lengths parallel and perpendicular to particle orientation.

Use params dictionary to set potential coefficients. The coefficients must be set per unique pair of particle types.

params

The Gay-Berne potential parameters. The dictionary has the following keys:

Type: TypeParameter [tuple [particle_type, particle_type], dict]

Example:

nl = nlist.Cell()
gay_berne = md.pair.GayBerne(nlist=nl, default_r_cut=2.5)
gay_berne.params[('A', 'A')] = dict(epsilon=1.0, lperp=0.45, lpar=0.5)
gay_berne.r_cut[('A', 'B')] = 2 ** (1.0 / 6.0)

property type_shapes

Get all the types of shapes in the current simulation.

Example

>>> gay_berne.type_shapes
[{'type': 'Ellipsoid', 'a': 1.0, 'b': 1.0, 'c': 1.5}]

Returns

A list of dictionaries, one for each particle type in the system.

(Loggable: category=”object”)