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md_chain_mts_lj.py
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md_chain_mts_lj.py
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#!/usr/bin/env python3
# md_chain_mts_lj.py
#------------------------------------------------------------------------------------------------#
# This software was written in 2016/17 #
# by Michael P. Allen <[email protected]>/<[email protected]> #
# and Dominic J. Tildesley <[email protected]> ("the authors"), #
# to accompany the book "Computer Simulation of Liquids", second edition, 2017 ("the text"), #
# published by Oxford University Press ("the publishers"). #
# #
# LICENCE #
# Creative Commons CC0 Public Domain Dedication. #
# To the extent possible under law, the authors have dedicated all copyright and related #
# and neighboring rights to this software to the PUBLIC domain worldwide. #
# This software is distributed without any warranty. #
# You should have received a copy of the CC0 Public Domain Dedication along with this software. #
# If not, see <http://creativecommons.org/publicdomain/zero/1.0/>. #
# #
# DISCLAIMER #
# The authors and publishers make no warranties about the software, and disclaim liability #
# for all uses of the software, to the fullest extent permitted by applicable law. #
# The authors and publishers do not recommend use of this software for any purpose. #
# It is made freely available, solely to clarify points made in the text. When using or citing #
# the software, you should not imply endorsement by the authors or publishers. #
#------------------------------------------------------------------------------------------------#
"""Molecular dynamics, multiple timesteps, chain molecule."""
def calc_variables ( ):
"""Calculates all variables of interest.
They are collected and returned as a list, for use in the main program.
"""
import numpy as np
import math
from averages_module import msd, cke, VariableType
# Preliminary calculations
kin = 0.5*np.sum(v**2)
rcm = np.sum ( r, axis=0 ) / n # Centre of mass
rsq = np.sum ( (r-rcm)**2 ) / n # Mean-squared distance from CM
eng = kin+total.pot+total_spr # Total energy
# Variables of interest, of class VariableType, containing three attributes:
# .val: the instantaneous value
# .nam: used for headings
# .method: indicating averaging method
# If not set below, .method adopts its default value of avg
# The .nam and some other attributes need only be defined once, at the start of the program,
# but for clarity and readability we assign all the values together below
# Internal energy per atom
# Total KE plus total LJ nonbonded energy plus total spring energy divided by N
e_f = VariableType ( nam = 'E/N', val = eng/n )
# Kinetic temperature
# Remove 6 degrees of freedom for conserved linear and angular momentum
t_k = VariableType ( nam = 'T kinetic', val = 2.0*kin/(3*n-6) )
# Radius of gyration
r_g = VariableType ( nam = 'Rg', val = math.sqrt(rsq) )
# MSD of kinetic energy, intensive
# Use special method to convert to Cv/N
c_f = VariableType ( nam = 'Cv/N', val = kin/math.sqrt(n), method = cke, instant = False )
# Mean-squared deviation of conserved energy per atom
conserved_msd = VariableType ( nam = 'Conserved MSD', val = eng/n,
method = msd, e_format = True, instant = False )
# Collect together for averaging
return [ e_f, t_k, r_g, c_f, conserved_msd ]
# Takes in a configuration of atoms in a linear chain (positions, velocities)
# NO periodic boundary conditions, no box
# Conducts molecular dynamics with springs and multiple timesteps
# Uses no special neighbour lists
# Reads several variables and options from standard input using JSON format
# Leave input empty "{}" to accept supplied defaults
# Input configuration, output configuration, all calculations, and all results
# are given in mass = 1 units, and in simulation units defined by the model
# For example, for Lennard-Jones, sigma = 1, epsilon = 1
# Despite the program name, there is nothing here specific to Lennard-Jones
# The model is defined in md_chain_lj_module.py
import json
import sys
import numpy as np
import math
from platform import python_version
from config_io_module import read_cnf_atoms, write_cnf_atoms
from averages_module import run_begin, run_end, blk_begin, blk_end, blk_add
from md_chain_lj_module import introduction, conclusion, zero_cm, force, spring, worst_bond, PotentialType
cnf_prefix = 'cnf.'
inp_tag = 'inp'
out_tag = 'out'
sav_tag = 'sav'
print('md_chain_mts_lj')
print('Python: '+python_version())
print('NumPy: '+np.__version__)
print()
print('Molecular dynamics, constant-NVE ensemble, chain molecule, multiple time steps')
print('Particle mass=1 throughout')
print('No periodic boundaries')
# Read parameters in JSON format
try:
nml = json.load(sys.stdin)
except json.JSONDecodeError:
print('Exiting on Invalid JSON format')
sys.exit()
# Set default values, check keys and typecheck values
defaults = {"nblock":10, "nstep":10000, "dt":0.0002, "k_spring":10000.0, "n_mts":10}
for key, val in nml.items():
if key in defaults:
assert type(val) == type(defaults[key]), key+" has the wrong type"
else:
print('Warning', key, 'not in ',list(defaults.keys()))
# Set parameters to input values or defaults
nblock = nml["nblock"] if "nblock" in nml else defaults["nblock"]
nstep = nml["nstep"] if "nstep" in nml else defaults["nstep"]
dt = nml["dt"] if "dt" in nml else defaults["dt"]
k_spring = nml["k_spring"] if "k_spring" in nml else defaults["k_spring"]
n_mts = nml["n_mts"] if "n_mts" in nml else defaults["n_mts"]
introduction()
# Write out parameters
print( "{:40}{:15d} ".format('Number of blocks', nblock) )
print( "{:40}{:15d} ".format('Number of steps per block', nstep) )
print( "{:40}{:15.6f}".format('Time step', dt) )
print( "{:40}{:15.6f}".format('Bond spring constant', k_spring) )
print( "{:40}{:15d} ".format('Multiple time step factor', n_mts) )
print( "{:40}{:15.6f}".format('Large time step', dt*n_mts) )
# Read in initial configuration
n, bond, r, v = read_cnf_atoms ( cnf_prefix+inp_tag, with_v=True)
print( "{:40}{:15d} ".format('Number of particles', n) )
print( "{:40}{:15.6f}".format('Bond length (in sigma units)', bond) )
r, v = zero_cm ( r, v )
print( "{:40}{:15.6f}".format('Worst bond length deviation', worst_bond(bond,r) ) )
# Initial forces, potential, etc plus overlap check
total, f = force ( r )
assert not total.ovr, 'Overlap in initial configuration'
total_spr, g = spring ( k_spring, bond, r )
# Initialize arrays for averaging and write column headings
run_begin ( calc_variables() )
for blk in range(1,nblock+1): # Loop over blocks
blk_begin()
for stp in range(nstep): # Loop over steps
# Single time step of length n_mts*dt
v = v + 0.5 * n_mts * dt * f # Kick half-step (long) with nonbonded forces f
for stp_mts in range(n_mts): # Loop over n_mts steps of length dt
v = v + 0.5 * dt * g # Kick half-step (short) with spring forces g
r = r + dt * v # Drift step (short)
total_spr, g = spring ( k_spring, bond, r ) # Evaluate spring forces g and potential
v = v + 0.5 * dt * g # Kick half-step (short) with spring forces g
total, f = force ( r ) # Evaluate nonbonded forces f and potential
assert not total.ovr, 'Overlap in configuration'
v = v + 0.5 * n_mts * dt * f # Kick half-step (long) with nonbonded forces f
blk_add ( calc_variables() )
blk_end(blk) # Output block averages
sav_tag = str(blk).zfill(3) if blk<1000 else 'sav' # Number configuration by block
write_cnf_atoms ( cnf_prefix+sav_tag, n, bond, r, v ) # Save configuration
run_end ( calc_variables() )
print( "{:40}{:15.6f}".format('Worst bond length deviation', worst_bond(bond,r) ) )
write_cnf_atoms ( cnf_prefix+out_tag, n, bond, r, v ) # Save configuration
conclusion()