TinyFF 0.2.2

Minimalistic Force Field evaluator.

TinyFF

This is a minimalistic force-field engine written in pure Python, using vectorized NumPy code. It has minimal dependencies (NumPy, SciPy, attrs and npy-append-array), so all the force-field specific code is self-contained.

This little library is geared towards teaching and favors simplicity and conciseness over fancy features and top-notch performance. TinyFF implements the nitty-gritty of linear-scaling pairwise potentials, so students can build their own molecular dynamics implementation, skipping the technicalities of implementing the correct potential energy, pressure and forces acting on atoms.

TinyFF is written by Toon Verstraelen for students of the Computational Physics course (C004504) in the Physics and Astronomy program at Ghent University. TinyFF is distributed under the conditions of the GPL-v3 license.

Installation

TinyFF is available on PyPI. In a properly configured Python virtual environment, you can install TinyFF with:

pip install tinyff

Features

TinyFF is a Python package with the following modules:

• tinyff.atomsmithy: functions for creating initial atomic positions.
• tinyff.forcefield: implements a pairwise force field: energy, atomic forces and pressure
• tinyff.neighborlist: used by the forcefield module to compute pairwise interactions with a real-space cut-off.
• tinyff.trajectory: tools for writing molecular dynamics trajectories to file(s).
• tinyff.utils: utility functions used in tinyff.

None of these modules implement any molecular dynamics integrators, nor any post-processing of molecular dynamics trajectories. This is the part that you are expected to write.

Basic usage

Computing energy, forces and pressure

The evaluation of the force field energy and its derivatives requires the following:

from tinyff.forcefield import CutoffWrapper, LennardJones, PairwiseForceField

# Define a pairwise potential, with energy and force shift
rcut = 5.0
lj = CutOffWrapper(LennardJones(2.5, 2.0), rcut)

# Define a force field
pwff = PairwiseForceFcell_lengthield(lj, rcut))

# You need atomic positions and the length of a periodic cell edge.
# The following line defines just two atomic positions.
atpos = np.array([[0.0, 0.0, 1.0], [1.0, 2.0, 0.0]])

# Note that the cell must be large enough to contain the cutoff sphere.
cell_length = 20.0

# Compute stuff:
# - The potential energy.
# - An array with Cartesian forces, same shape as `atpos`.
# - The force contribution the pressure
#   (often the written as the second term in the virial pressure).
potential_energy, forces, frc_pressure = pwff(atpos, cell_length)

This basic recipe can be extended by passing additional options into the PairwiseForceField constructor:

• Linear-scaling neighbor lists with the linked-cell method, a.k.a. cell lists:

  from tinyff.neighborlist import build_nlist_linked_cell
  
  # Construct your force field object as follows:
  pwff = PairwiseForceField(lj, rcut, build_nlist=build_nlist_linked_cell))
  

  Note that the current linked-cell implementation is not very efficient (yet), so we currently do not recommended using it. (For about 1500 atoms, it becomes more efficient than the naive implementation.)

• Verlet lists (cut-off radius + buffer):

  rmax = 6.0  # > rcut, so buffer of 1.0
  pwff = PairwiseForceField(lj, rmax, nlist_reuse=16))
  

Forging initial positions

The atomsmithy module can generate a cubic box with standard lattices or randomized atomic positions:

from tinyff.atomsmithy import (
    build_bcc_lattice,
    build_cubic_lattice,
    build_fcc_lattice,
    build_random_cell,
)

# Atoms on a regular lattice. args:
# - primitive cell edge length
# - number of repetitions in X, Y and Z directions.
atpos = build_cubic_lattice(2.5, 2)
atpos = build_bcc_lattice(2.5, 3)
atpos = build_fcc_lattice(2.5, 4)

# Randomize positions. args:
# - cell edge length
# - number of atoms
atpos = build_random_cell(10.0, 32)

Writing trajectories to disk

For visualization with nglview, TinyFF provides a PDBWriter, to be used as follows:

from tinyff.trajectory import PDBWriter

# Initialization of the writer: specify a file and a conversion factor to angstrom.
# If the PDB file exists, it is overwritten!
# This example shows the conversion factor when your program works in nanometer.
# Through `atnums` you can specify the chemical elements, here 50 argon atoms (Z=18).
pdb_writer = PDBWriter("trajectory.pdb", to_angstrom=10.0, atnums=[18] * 50)

# Somewhere in your code, typically inside some loop.
# cell_length(s) can be a float or an array of 3 floats.
pdb_writer.dump(atpos, cell_length)

# If you are using Jupyter, you can visualize the trajectory with nglview as follows:
import mdtraj
import nglview
traj = mdtraj.load(f"trajectory.pdb")
view = nglview.show_mdtraj(traj)
view.clear()
view.add_hyperball()
view.add_unitcell()
view

For numerical post-processing, TinyFF provides a more flexible trajectory writer, which writes NPY files. It is implemented with the npy-append-array library, which makes it possible to extend an NPY file without having to rewrite from scratch. (That would not be possible with NumPy alone.) The NPYWriter can be used as follows:

from tinyff.trajectory import NPYWriter

# Initialization, will create (and possibly clean up an existing) a `traj` directory.
npy_writer = NPYWriter("traj")

# Somewhere in your production code, normally inside some loop.
# You can specify any array or float you like,
# as long as the shape and type is the same upon every call.
# This will result in files `traj/atpos.npy`, `traj/pressure.npy`, etc.
# These files will contain arrays with data passed into all `dump` calls.
npy_writer.dump(atpos=atpos, pressure=pressure, temperature=temperature, ...)

# In your post-processing code
pressure = np.load("traj/pressure.npz")
print(np.mean(pressure))

Irrespective of the file format, it is recommended to write trajectory data only every so many steps. The constructors of both trajectory writers have an optional stride argument to control the frequency of the output. For example:

npy_writer = NPYWriter("traj", stride=100)

Change log

The history of changes can be found in CHANGELOG.md.