md.nlist

Overview

`md.nlist.NList`	Base class neighbor list.
`md.nlist.Cell`	Neighbor list computed via a cell list.
`md.nlist.Stencil`	Cell list based neighbor list using stencils.
`md.nlist.Tree`	Bounding volume hierarchy based neighbor list.

Details

Neighbor list acceleration structures.

Pair forces (hoomd.md.pair) use neighbor list data structures to perform efficient calculations. HOOMD-blue provides a several types of neighbor list construction algorithms that you can select from. Multiple pair force objects can share a neighbor list, or use independent neighbor list objects. When neighbor lists are shared, they find neighbors within the the maximum \(r_{\mathrm{cut},i,j}\) over the associated pair potentials.

class hoomd.md.nlist.Cell(buffer, exclusions=('bond',), rebuild_check_delay=1, diameter_shift=False, check_dist=True, max_diameter=1.0, deterministic=False)

Neighbor list computed via a cell list.

Parameters

buffer (float) – Buffer width \([\mathrm{length}]\).
exclusions (tuple[str]) – Defines which particles to exlclude from the neighbor list, see more details in NList.
rebuild_check_delay (int) – How often to attempt to rebuild the neighbor list.
diameter_shift (bool) – Flag to enable / disable diameter shifting.
check_dist (bool) – Flag to enable / disable distance checking.
max_diameter (float) – The maximum diameter a particle will achieve \([\mathrm{length}]\).
deterministic (bool) – When True, sort neighbors to help provide deterministic simulation runs.

Cell finds neighboring particles using a fixed width cell list, allowing for O(kN) construction of the neighbor list where k is the number of particles per cell. Cells are sized to the largest \(r_\mathrm{cut}\). This method is very efficient for systems with nearly monodisperse cutoffs, but performance degrades for large cutoff radius asymmetries due to the significantly increased number of particles per cell.

Examples:

cell = nlist.Cell()

deterministic

When True, sort neighbors to help provide deterministic simulation runs.

Type: bool

class hoomd.md.nlist.NList(buffer, exclusions, rebuild_check_delay, diameter_shift, check_dist, max_diameter)

Base class neighbor list.

Methods and attributes provided by this base class are available to all subclasses.

Attention

Users should instantiate the subclasses, using NList directly will result in an error.

Buffer distance

Set the buffer distance to amortize the cost of the neighbor list build. When buffer > 0, a neighbor list computed on one step can be reused on subsequent steps until a particle moves a distance buffer/2. When check_dist is True, NList starts checking how far particles have moved rebuild_check_delay time steps after the last build and performs a rebuild when any particle has moved a distance buffer/2. When check_dist is False, NList always rebuilds after rebuild_check_delay time steps.

Exclusions

Neighbor lists nominally include all particles within the specified cutoff distances. The exclusions attribute defines which particles will be excluded from the list, even if they are within the cutoff. exclusions is a tuple of strings that enable one more more types of exclusions. The valid exclusion types are:

bond: Exclude particles that are directly bonded together.
angle: Exclude the first and third particles in each angle.
constraint: Exclude particles that have a distance constraint applied between them.
dihedral: Exclude the first and fourth particles in each dihedral.
special_pair: Exclude particles that are part of a special pair.
body: Exclude particles that belong to the same rigid body.
1-3: Exclude particles i and k whenever there is a bond (i,j) and a bond (j,k).
1-4: Exclude particles i and m whenever there are bonds (i,j), (j,k), and (k,m).

buffer

Buffer width \([\mathrm{length}]\).

Type: float

exclusions

Defines which particles to exlclude from the neighbor list, see more details above.

Type: tuple[str]

rebuild_check_delay

How often to attempt to rebuild the neighbor list.

Type: int

diameter_shift

Flag to enable / disable diameter shifting.

Type: bool

check_dist

Flag to enable / disable distance checking.

Type: bool

max_diameter

The maximum diameter a particle will achieve \([\mathrm{length}]\).

Type: float

property shortest_rebuild

The shortest period between neighbor list rebuilds.

shortest_rebuild is the smallest number of time steps between neighbor list rebuilds during the previous Simulation.run.

(Loggable: category=”scalar”)

Type: int

class hoomd.md.nlist.Stencil(cell_width, buffer, exclusions=('bond',), rebuild_check_delay=1, diameter_shift=False, check_dist=True, max_diameter=1.0, deterministic=False)

Cell list based neighbor list using stencils.

Parameters

cell_width (float) – The underlying stencil bin width for the cell list \([\mathrm{length}]\).
buffer (float) – Buffer width \([\mathrm{length}]\).
exclusions (tuple[str]) – Defines which particles to exlclude from the neighbor list, see more details in NList.
rebuild_check_delay (int) – How often to attempt to rebuild the neighbor list.
diameter_shift (bool) – Flag to enable / disable diameter shifting.
check_dist (bool) – Flag to enable / disable distance checking.
max_diameter (float) – The maximum diameter a particle will achieve \([\mathrm{length}]\).
deterministic (bool) – When True, sort neighbors to help provide deterministic simulation runs.

Stencil creates a cell list based neighbor list object to which pair potentials can be attached for computing non-bonded pairwise interactions. Cell listing allows for O(N) construction of the neighbor list. Particles are first spatially sorted into cells with the given width cell_width.

M.P. Howard et al. 2016 describes this neighbor list implementation in HOOMD-blue. Cite it if you utilize this neighbor list style in your work.

This neighbor list style differs from Cell in how the adjacent cells are searched for particles. One stencil is computed per particle type based on the value of cell_width set by the user, which defines the bins that the particle must search in. Distances to the bins in the stencil are precomputed so that certain particles can be quickly excluded from the neighbor list, leading to improved performance compared to Cell when there is size disparity in the cutoff radius. The memory demands of Stencil can also be lower than Cell if your system is large and has many small cells in it; however, Tree is usually a better choice for these systems.

The performance of Stencil depends strongly on the choice of cell_width. The best performance is obtained when the cutoff radii are multiples of the cell_width, and when the cell_width covers the simulation box with a roughly integer number of cells. The cell_width must be set manually.

Examples:

nl_s = nlist.Stencil(cell_width=1.5)

cell_width

The underlying stencil bin width for the cell list \([\mathrm{length}]\).

Type: float

deterministic

When True, sort neighbors to help provide deterministic simulation runs.

Type: bool

class hoomd.md.nlist.Tree(buffer, exclusions=('bond',), rebuild_check_delay=1, diameter_shift=False, check_dist=True, max_diameter=1.0)

Bounding volume hierarchy based neighbor list.

Parameters

buffer (float) – Buffer width \([\mathrm{length}]\).
exclusions (tuple[str]) – Defines which particles to exlclude from the neighbor list, see more details in NList.
rebuild_check_delay (int) – How often to attempt to rebuild the neighbor list.
diameter_shift (bool) – Flag to enable / disable diameter shifting.
check_dist (bool) – Flag to enable / disable distance checking.
max_diameter (float) – The maximum diameter a particle will achieve \([\mathrm{length}]\).

Tree creates a neighbor list using a bounding volume hierarchy (BVH) tree traversal. A BVH tree of axis-aligned bounding boxes is constructed per particle type, and each particle queries each tree to determine its neighbors. This method of searching leads to significantly improved performance compared to cell lists in systems with moderate size asymmetry, but has slightly poorer performance (10% slower) for monodisperse systems. Tree can also be slower than Cell if there are multiple types in the system, but the cutoffs between types are identical. (This is because one BVH is created per type.) The user should carefully benchmark neighbor list build times to select the appropriate neighbor list construction type.

M.P. Howard et al. 2016 describes the original implementation of this algorithm for HOOMD-blue. M.P. Howard et al. 2019 describes the improved algorithm that is currently implemented. Cite both if you utilize this neighbor list style in your work.

Examples:

nl_t = nlist.Tree(check_dist=False)