Liquid Si - Freezing: Difference between revisions
| Line 247: | Line 247: | ||
print temp,d/time | print temp,d/time | ||
We will use a short bash script (''dscript.sh'') to calculate the diffusion coefficient at different temperatures and plot them in a file. To execute it just type "''./dscript.sh''". | We will use a short bash script (''dscript.sh'') to calculate the diffusion coefficient at different temperatures and plot them in a file (diff_coeff.jpg): | ||
#!/bin/bash | |||
if test -f "diff_coeff.dat"; then | |||
rm diff_coeff.dat | |||
fi | |||
touch diff_coeff.dat | |||
for i in 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000; do | |||
diffusion_coefficient.py XDATCAR.$i >> diff_coeff.dat | |||
done | |||
gnuplot -e "set terminal jpeg; set key left; set xlabel 'time (ps)'; set ylabel 'D (Ang^2/ps)'; set style data lines; plot 'diff_coeff.dat' " > diff_coeff.jpg | |||
To execute it just type "''./dscript.sh''". | |||
=== Pair correlation function === | === Pair correlation function === | ||
Revision as of 09:18, 4 July 2019
Overview >Liquid Si - Standard MD > Liquid Si - Freezing > Nucleophile Substitution CH3Cl - Standard MD > Nuclephile Substitution CH3Cl - mMD1 > Nuclephile Substitution CH3Cl - mMD2 > Nuclephile Substitution CH3Cl - mMD3 > Nuclephile Substitution CH3Cl - SG > Nuclephile Substitution CH3Cl - BM > List of tutorials
Task
In this example the goal is to simulate the freezing of liquid Si.
Input
POSCAR
Si
15.12409564534287297131
0.5000000000000000 0.5000000000000000 0.0000000000000000
0.0000000000000000 0.5000000000000000 0.5000000000000000
0.5000000000000000 0.0000000000000000 0.5000000000000000
48
Direct
0.8550657259653851 0.3204575801875221 0.6180363868822553
0.6045454476433229 0.0546379652195404 0.1629680405553871
0.4803889256776521 0.2999635319377835 0.0131251454718051
0.8413504226620471 0.7598095803296524 0.1917781560970181
0.9754163118144437 0.6134171268457649 0.7421364242876367
0.2668229391055025 0.0066502741664650 0.0031140604380929
0.8935777664000575 0.3324172908647429 0.9535738516718881
0.0527608886321274 0.5249316429131962 0.5293744880144071
0.4396089233132741 0.7564833235979471 0.5665855438788387
0.5907859878830199 0.5198033580597228 0.3581725847640679
0.2120832721474721 0.4042899613004446 0.7921535013319151
0.0225803885096466 0.8414911198321031 0.1209255489569852
0.0992500701525566 0.3917384466892963 0.3612433325214984
0.9673794138223195 0.5206425706394114 0.1719623236201897
0.2774602656926126 0.8480860088162007 0.2673309412777037
0.0196991774214161 0.8282178425383616 0.6986213756952502
0.3570927152895376 0.2951488295546784 0.2651851032568589
0.1663829731894614 0.9766237917413699 0.6051764245375237
0.4931841331696695 0.8689890620771937 0.2612357008392290
0.8006473407426477 0.1033419073227807 0.4706563716777467
0.0161340851939779 0.9953827418297991 0.8853439845676159
0.7827740166661069 0.1821830067208054 0.9399555168314748
0.0720651739141343 0.2539424963694544 0.6857919074323433
0.4443385370769313 0.0486404637002326 0.4180706114402839
0.7055263679666055 0.6802623819082319 0.7983614866719116
0.2237125282521105 0.4055474352416297 0.0077044950891134
0.2963682069847125 0.5771265542042112 0.2019757061665083
0.2782449529809642 0.0451513130915826 0.7644934848784113
0.9312079203181675 0.9090938018377080 0.3429249881187518
0.6341882597200124 0.2969253226419481 0.3227590981305088
0.3587691103780569 0.1061057273904179 0.0931868777500710
0.8710437838676732 0.6541301230631744 0.4261617089364881
0.6784300588817769 0.3263889355408940 0.5560491395978739
0.5597052314845080 0.0174390112509929 0.6129003207931863
0.0595962318875451 0.1019295953521402 0.3340999072062676
0.7689671766774326 0.1768870209149794 0.1604177484299765
0.9603661624482890 0.3311649224573259 0.1439224909303592
0.3792868784787023 0.2806150985211180 0.4921541531665999
0.8079860889823454 0.9194188799048340 0.9131036494263627
0.3002081239026374 0.7834053620019006 0.8650323716139056
0.4704528574512951 0.7221628305989689 0.9746107190983403
0.2886552568292480 0.5927625600330780 0.4239421203107919
0.4116743942942291 0.2198943758058664 0.7072597030225044
0.2104494234814825 0.6457654201409418 0.8275863924787099
0.6784628197745537 0.7205455185203838 0.1093053357228383
0.6344130299021448 0.1650970001101275 0.8037018707797643
0.3965793440603315 0.5364088146415013 0.6064549771969059
0.6686412136025504 0.7848666926903073 0.5681234351534038
INCAR
SYSTEM = Si # electronic degrees LREAL = A # real space projection PREC = Normal # chose Low only after tests EDIFF = 1E-5 # do not use default (too large drift) ISMEAR = -1 ; SIGMA = 0.130 # Fermi smearing: 1500 K 0.086 10-3 ALGO = Very Fast # recommended for MD (fall back ALGO = Fast) MAXMIX = 40 # reuse mixer from one MD step to next ISYM = 0 # no symmetry NELMIN = 4 # minimum 4 steps per time step, avoid breaking after 2 steps # MD (do little writing to save disc space) IBRION = 0 # main molecular dynamics tag NSW = 400 # number of MD steps POTIM = 3 # time step of MD NWRITE = 0 # controls output NBLOCK = 10 # after ten steps pair correlation function is written out LCHARG = .FALSE. # no charge density written out LWAVE = .FALSE. # no wave function coefficients written out TEBEG = $i # starting temperature for MD TEEND = $i # end temperature for MD # canonic (Nose) MD with XDATCAR updated every 10 steps MDALGO = 2 ä switch to select thermostat SMASS = 3 # Nose mass ISIF = 2 # this tag selects the ensemble in combination with the thermostat
- Most of the tags here are very similar to the tags used in the previous example (Liquid Si - Standard MD.
- A stepwise cooling will be applied in this example via a script where $i for TEBEG and TEEND will be replaced in each calculation (see below).
KPOINTS
test 0 0 0 monk 1 1 1 0 0 0
- A single k-point is sufficient in this example.
Calculation
We will execute the cooling stepwise so several calculations at different temperatures are required in this calculation. The INCAR is created with a script for each temperature and run separately. After each step the important files are saved to file.$i, where $i are the temperatures ranging from 2000 to 800 K in steps of 100 K. The script running the calculations looks like the following:
for i in 2000 1900 1800 1700 1600 1500 1400 1300 1200 1100 1000 900 800 do cat >INCAR <<! SYSTEM = Si # electronic degrees LREAL = A # real space projection PREC = Normal # chose Low only after tests EDIFF = 1E-5 # do not use default (too large drift) ISMEAR = -1 ; SIGMA = 0.130 # Fermi smearing: 1500 K 0.086 10-3 ALGO = Very Fast # recommended for MD (fall back ALGO = Fast) MAXMIX = 40 # reuse mixer from one MD step to next ISYM = 0 # no symmetry NELMIN = 4 # minimum 4 steps per time step, avoid breaking after 2 steps # MD (do little writing to save disc space) IBRION = 0 # main molecular dynamics tag NSW = 400 # number of MD steps POTIM = 3 # time step of MD NWRITE = 0 # controls output NBLOCK = 10 # after ten steps pair correlation function is written out LCHARG = .FALSE. # no charge density written out LWAVE = .FALSE. # no wave function coefficients written out TEBEG = $i # starting temperature for MD TEEND = $i # end temperature for MD # canonic (Nose) MD with XDATCAR updated every 10 steps MDALGO = 2 ä switch to select thermostat SMASS = 3 # Nose mass ISIF = 2 # this tag selects the ensemble in combination with the thermostat ! mpirun -np 2 /path/to/your/vasp/executable cp XDATCAR XDATCAR.$i cp OUTCAR OUTCAR.$i cp PCDAT PCDAT.$i cp CONTCAR CONTCAR.$i cp POSCAR POSCAR.$i cp OSZICAR OSZICAR.$i cp CONTCAR POSCAR done
- Before running the script one has to replace "'/path/to/your/vasp/executable'" by the path to his "'vasp_gam'" executable. The script is then simply starte by typing "'./script'" on the command line.
Diffusion
The diffusion coefficient in 3 dimensions is given as
where t defines time and . The 6 in the denominator contains a factor of 3 accounting for the 3 spatial dimensions (usually the diffusion coefficient is written with a 2 in the denominator in literature corresponding to only one dimension). In our case we calculate the above equation as follows
.
Here the diffusion coefficient is calculated over an ensemble average to get better statistics. Our calculations were carried out for 1200 fs for each temperature. We will average in our case over the last 900 fs regarding the first 300 fs as equilibration of each temperature. The following python script (diffusion_coefficient.py) calculates the diffusion coefficient at a given temperature:
#!/usr/bin/python
import sys
import re
import math
#setting grid for histogram
potim = 3 #timestep from INCAR file
readfile = open(sys.argv[1],"r") #input XDATCAR file in format XDATCAR.TEMP
temp=re.sub("XDATCAR.",,sys.argv[1]) #extracts temperature from input file name
z=0 #counter
natoms=0 #number of atoms in XDATCAR file
posion = [] #atom positions in Cartesian coordinates
confcount = 0 #number of structures in XDATCAR file
direct=[] #number of time steps for each structure in XDATCAR file
a=[] #lattice parameter in 1st dimension
b=[] #lattice parameter in 2nd dimension
c=[] #lattice parameter in 3rd dimension
#read in XDATCAR file
line=readfile.readline()
while (line):
z=z+1
line.strip()
line=re.sub('^',' ',line)
y=line.split()
if (z==2):
scale=float(y[0])
if (z==3):
a.append(float(y[0]))
a.append(float(y[1]))
a.append(float(y[2]))
a_len=(a[0]*a[0]+a[1]*a[1]+a[2]*a[2])**0.5
if (z==4):
b.append(float(y[0]))
b.append(float(y[1]))
b.append(float(y[2]))
b_len=(b[0]*b[0]+b[1]*b[1]+b[2]*b[2])**0.5
if (z==5):
c.append(float(y[0]))
c.append(float(y[1]))
c.append(float(y[2]))
c_len=(c[0]*c[0]+c[1]*c[1]+c[2]*c[2])**0.5
if (z==7):
natoms=int(y[0])
if (y[0]=="Direct"):
direct.append(int(y[2]))
posion.append([])
for i in range(0,natoms):
line=readfile.readline()
line.strip()
line=re.sub('^',' ',line)
f=line.split()
cartpos_x=a[0]*float(f[0])+a[1]*float(f[1])+a[2]*float(f[2])
cartpos_y=b[0]*float(f[0])+b[1]*float(f[1])+b[2]*float(f[2])
cartpos_z=c[0]*float(f[0])+c[1]*float(f[1])+c[2]*float(f[2])
#positions of ions for each structure are obtained here
posion[confcount].append([cartpos_x,cartpos_y,cartpos_z])
confcount=confcount+1
line=readfile.readline()
readfile.close
#calculate diffusion coefficient
#skip first 10 configurations corresponding to 300 fs
d=0.0
for i in range(10,confcount):
for j in range(0,natoms):
x_diff=posion[i][j][0]-posion[0][j][0]
if (abs(x_diff)>(0.5*a_len)):
if (x_diff<0):
x_diff=x_diff+a_len
elif (x_diff>0):
x_diff=x_diff-a_len
y_diff=posion[i][j][1]-posion[0][j][1]
if (abs(y_diff)>(0.5*b_len)):
if (y_diff<0):
y_diff=y_diff+b_len
elif (y_diff>0):
y_diff=y_diff-b_len
z_diff=posion[i][j][2]-posion[0][j][2]
if (abs(z_diff)>(0.5*c_len)):
if (z_diff<0):
z_diff=z_diff+c_len
elif (x_diff>0):
z_diff=z_diff-c_len
d=d+x_diff**2.0+y_diff**2.0+z_diff**2.0
#print diffusion coefficient (in Ang^2/ps) vs temperature (in K)
d=d/(confcount-1-10)/natoms/6.0
time=(direct[confcount-1]-direct[10])*potim/10**3.0 #conversion to ps
print temp,d/time
We will use a short bash script (dscript.sh) to calculate the diffusion coefficient at different temperatures and plot them in a file (diff_coeff.jpg):
#!/bin/bash if test -f "diff_coeff.dat"; then rm diff_coeff.dat fi touch diff_coeff.dat for i in 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000; do diffusion_coefficient.py XDATCAR.$i >> diff_coeff.dat done gnuplot -e "set terminal jpeg; set key left; set xlabel 'time (ps)'; set ylabel 'D (Ang^2/ps)'; set style data lines; plot 'diff_coeff.dat' " > diff_coeff.jpg
To execute it just type "./dscript.sh".
Pair correlation function
The pair-correlation function provides information about the probability of finding two atoms at a given distance . The pair-correlation function written on PCDAT.T should be processed using the script PCDATtoPCDATxy:
awk <PCDAT >PCDAT.xy '
NR==8 { pcskal=$1}
NR==9 { pcfein=$1}
NR>=13 {
line=line+1
if (line==257) {
print " "
line=0
}
else
print (line-0.5)*pcfein/pcskal,$1
}
'
Mind: You will have to set the correct path to your VASP executable and invoke VASP with the correct command (e.g., in the above: mpirun -np 2).