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mc_chain_nvt_cbmc_lj.py
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#!/usr/bin/env python3
# mc_chain_nvt_cbmc_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. #
#------------------------------------------------------------------------------------------------#
"""Monte Carlo, single chain, NVT, CBMC."""
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, VariableType
from mc_chain_lj_module import potential, spring_pot, PotentialType
# Preliminary calculations
total = potential(r) # Nonbonded potential with overlap flag
assert not total.ovr, 'Overlap in configuration'
spr = spring_pot ( bond, k_spring, r ) # Total spring potential energy
rcm = np.sum ( r, axis=0 ) / n # Centre of mass
rsq = np.sum( (r - rcm)**2 ) / n # Mean-squared distance from CM
# 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
# Move acceptance ratio
m_r = VariableType ( nam = 'Regrow ratio', val = m_ratio, instant = False )
# Total potential energy per atom (excess, without ideal gas contribution)
# Total PE of bond springs plus total LJ PE (not cut, nor shifted) divided by N
e_x = VariableType ( nam = 'PE/N', val = (spr+total.pot)/n )
# Radius of gyration
r_g = VariableType ( nam = 'Rg', val = np.sqrt(rsq) )
# Heat Capacity per atom (excess, without ideal gas contribution)
# MSD of PE / (sqrt(N)*T)
# Total PE of bond springs plus total LJ PE (not cut, nor shifted), divided by T
c_x = VariableType ( nam = 'Cv(ex)/N', val = (spr+total.pot)/(np.sqrt(n)*temperature),
method = msd, instant = False )
# Collect together into a list for averaging
return [ m_r, e_x, r_g, c_x ]
# Takes in a configuration of atom positions in a linear chain
# NO periodic boundary conditions, no box
# Conducts Monte Carlo, NVT ensemble using CBMC regrowth moves
# 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 simulation units defined by the model.
# E.g. for Lennard-Jones, atomic diameter sigma = 1, well-depth epsilon=1
# Configurational weights are calculated on the basis of the nonbonded interactions
# Despite the program name, there is nothing here specific to Lennard-Jones
# The model is defined in mc_chain_lj_module
import json
import sys
import numpy as np
import math
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 mc_chain_lj_module import introduction, conclusion, regrow
cnf_prefix = 'cnf.'
inp_tag = 'inp'
out_tag = 'out'
sav_tag = 'sav'
print('mc_chain_nvt_cbmc_lj')
print('Monte Carlo, constant-NVT ensemble, CBMC, chain molecule')
print('Simulation uses full nonbonded potential (no cutoff)')
# 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":5000, "m_max":3, "k_max":32,
"temperature":1.0, "k_spring":400.0}
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"]
m_max = nml["m_max"] if "m_max" in nml else defaults["m_max"]
k_max = nml["k_max"] if "k_max" in nml else defaults["k_max"]
temperature = nml["temperature"] if "temperature" in nml else defaults["temperature"]
k_spring = nml["k_spring"] if "k_spring" in nml else defaults["k_spring"]
introduction()
np.random.seed()
# Write out parameters
print( "{:40}{:15d} ".format('Number of blocks', nblock) )
print( "{:40}{:15d} ".format('Number of steps per block', nstep) )
print( "{:40}{:15d} ".format('Max atoms in regrow', m_max) )
print( "{:40}{:15d} ".format('Random tries per atom in regrow', k_max) )
print( "{:40}{:15.6f}".format('Specified temperature', temperature) )
print( "{:40}{:15.6f}".format('Bond spring strength', k_spring) )
# Read in initial configuration
n, bond, r = read_cnf_atoms ( cnf_prefix+inp_tag)
print( "{:40}{:15d} ".format('Number of particles', n) )
print( "{:40}{:15.6f}".format('Bond length (in sigma units)', bond) )
# Initialize arrays for averaging and write column headings
m_ratio = 0.0
run_begin ( calc_variables() )
for blk in range(1,nblock+1): # Loop over blocks
blk_begin()
for stp in range(nstep): # Loop over steps
r, accepted = regrow ( temperature, m_max, k_max, bond, k_spring, r )
m_ratio = 1.0 if accepted else 0.0
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 ) # Save configuration
run_end ( calc_variables() )
write_cnf_atoms ( cnf_prefix+out_tag, n, bond, r ) # Save configuration
conclusion()