def test_LennardJones():
pe_theory = theory_lj(distances, epsilon=2, sigma=3.5, cutoff=11)
simulation = pyMD.Simulation(
positions=positions,
masses=masses,
box=box,
velocities=velocities
)
simulation.set_integrator(pyMD.VelocityVerletIntegrator(1.0))
simulation.add_pair_style(pyMD.LennardJones(epsilon=2, sigma=3.5, cutoff=11))
simulation._init_step()
simulation.positions[0] = (0, 0, 0)
for i, d in enumerate(distances):
simulation.positions[1] = (d, 0, 0)
# simulation.neigh.apply_pbc()
# simulation.neigh.build_neighborlist()
pe[i] = simulation.pair_styles[0].get_current_state(return_forces=False)
np.testing.assert_almost_equal(pe, pe_theory)
def test_simulation_LJ_melt(verbose=False):
path_reference = os.path.join('resources', 'LJ_melt')
dump_file = os.path.join(path_reference, 'dump.lammpstrj.gz')
xyz_lammps, pe_lammps, force_lammps = get_lammps_data(dump_file)
box = [
[16.79596191382507, 0, 0],
[0, 16.79596191382507, 0],
[0, 0, 16.79596191382507],
]
positions = np.zeros((4000, 3), dtype=np.float64)
positions[:] = xyz_lammps[0]
velocities = np.zeros(positions.shape, dtype=np.float64)
masses = 1.0 * np.ones(len(positions))
simulation = pyMD.Simulation(positions=positions,
masses=masses,
box=box,
velocities=velocities)
simulation.nn_settings['skin'] = 0.3
simulation.set_integrator(pyMD.VelocityVerletIntegrator(0.005))
simulation.add_pair_style(
pyMD.LennardJones(epsilon=1, sigma=1.0, cutoff=2.5, shift=False))
simulation._init_step()
pe_pyMD = np.zeros(pe_lammps.shape)
force_pyMD = np.zeros(force_lammps.shape)
for i, xyz in enumerate(xyz_lammps):
if verbose and not i % len(xyz_lammps) / 10:
print('\r{}/{}'.format(i + 1, len(xyz_lammps)), end='', flush=True)
simulation.positions[:] = xyz
simulation.neigh.build_neighborlist()
force, pe = simulation.pair_styles[0].get_current_state(
return_forces=True)
pe_pyMD[i] = pe
force_pyMD[i] = force
np.testing.assert_array_almost_equal(pe_lammps,
pe_pyMD,
err_msg='Potential Energy mismatch')
np.testing.assert_array_almost_equal(force_lammps,
force_pyMD,
err_msg='Forces mismatch')
if verbose:
print('LAMMPS and pyMD results match')
def test_simulation_distance_2LJ_beads():
path_reference = os.path.join('resources', 'distance_2LJ_beads')
dump_file = os.path.join(path_reference, 'dump.lammpstrj.gz')
xyz_lammps, pe_lammps, force_lammps = get_lammps_data(dump_file)
box = [
[6.0, 0, 0],
[0, 6.0, 0],
[0, 0, 6.0],
]
positions = np.array([
[0, 0, 0],
[0.4, 0, 0],
], dtype=np.float64)
velocities = np.array([
[0, 0, 0],
[0, 0, 0]
], dtype=np.float64)
masses = 1.0 * np.ones(len(positions))
simulation = pyMD.Simulation(
positions=positions,
masses=masses,
box=box,
velocities=velocities
)
simulation.nn_settings['skin'] = 0.3
simulation.set_integrator( pyMD.VelocityVerletIntegrator(0.005))
simulation.add_pair_style( pyMD.LennardJones(epsilon=1, sigma=1.0, cutoff=2.5, shift=False))
simulation._init_step()
pe_pyMD = np.zeros(pe_lammps.shape)
force_pyMD = np.zeros(force_lammps.shape)
for i, xyz in enumerate(xyz_lammps):
simulation.positions[:] = xyz
simulation.neigh.build_neighborlist()
force, pe = simulation.pair_styles[0].get_current_state(return_forces=True)
pe_pyMD[i] = pe
force_pyMD[i] = force
np.testing.assert_array_almost_equal(pe_lammps, pe_pyMD, err_msg='Potential Energy mismatch')
np.testing.assert_array_almost_equal(force_lammps, force_pyMD, err_msg='Forces mismatch')
system = pyMD.builder.RandomBeadsBox(n_atoms=n_atoms,
sidelength=20,
mode='mc',
mc_r_min=3.0)
box = system.box
positions = system.positions
masses = 1 * np.ones(n_atoms)
temperature = 0.02 # just a scaling atm
velocities = pyMD.builder.create_velocities(n_atoms, masses, temperature)
simulation = pyMD.Simulation(positions=positions,
masses=masses,
box=box,
velocities=velocities)
simulation.set_integrator(pyMD.VelocityVerletIntegrator(0.005, units='LJ'))
simulation.add_pair_style(pyMD.LennardJones(epsilon=0.25, sigma=1, cutoff=2.5))
#simulation.add_reporter( pyMD.InMemoryReporter(n_dump=1000) )
#simulation.add_reporter( pyMD.ThermodynamicsReporter(n_dump=1000) )
simulation.add_reporter(
pyMD.LammpsTrajectoryReporter('test.lammpstrj', n_dump=100))
simulation.minimize(10000)
#===================================================================#
# run the simulation
simulation.run(int(1e5))
velocities = np.array([
[ 5e-5, 0, 0],
[-5e-5, 0, 0]
], dtype=np.float64)
masses = 18.0*np.ones(len(positions))
#===================================================================#
# Define the Simulation
simulation = pyMD.Simulation(
positions=positions,
masses=masses,
box=box,
velocities=velocities
)
simulation.set_integrator( pyMD.VelocityVerletIntegrator(1.0, units='real') )
simulation.add_pair_style( pyMD.LennardJones(epsilon=1e-3, sigma=3.5, cutoff=11) )
simulation.add_reporter( pyMD.InMemoryTrajectoryReporter(n_dump=1000) )
simulation.add_reporter( pyMD.InMemoryThermodynamicsReporter(n_dump=1000) )
# visualzie with : vmd -lammpstrj test.lammpstrj
simulation.add_reporter( pyMD.LammpsTrajectoryReporter('test.lammpstrj', n_dump=1000))
#===================================================================#
# run the simulation
simulation.run(int(1e6))
pe[i] = simulation.pair_styles[0].get_current_state(return_forces=False)
np.testing.assert_almost_equal(pe, pe_theory)
if __name__ == '__main__':
pe_theory = theory_lj(distances, epsilon=2, sigma=3.5, cutoff=11)
simulation = pyMD.Simulation(
positions=positions,
masses=masses,
box=box,
velocities=velocities
)
simulation.set_integrator(pyMD.VelocityVerletIntegrator(1.0))
simulation.add_pair_style(pyMD.LennardJones(epsilon=2, sigma=3.5, cutoff=11))
simulation._init_step()
simulation.positions[0] = (0, 0, 0)
for i, d in enumerate(distances):
simulation.positions[1] = (d, 0, 0)
# simulation.neigh.apply_pbc()
# simulation.neigh.build_neighborlist()
pe[i] = simulation.pair_styles[0].get_current_state(return_forces=False)
import matplotlib.pyplot as plt
plt.plot(distances, pe, label='pyMD')