Liquid Si - MLFF: Difference between revisions

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In this example we start from a 64 atom super cell of diamond-fcc Si (the same as in {{TAG|Liquid Si - Standard MD}}):
In this example we start from a 64 atom super cell of diamond-fcc Si (the same as in {{TAG|Liquid Si - Standard MD}}):
  Si cubic diamond 2x2x2 super cell of conventional cell
  Si_CD_2x2x2
       5.43090000000000
       5.43090000000000
     2.00000000  0.00000000  0.00000000
     2.00000000  0.00000000  0.00000000
Line 97: Line 97:
  {{TAGBL|SIGMA}} = 0.1
  {{TAGBL|SIGMA}} = 0.1
  {{TAGBL|LREAL}} = Auto
  {{TAGBL|LREAL}} = Auto
{{TAGBL|ALGO}} = FAST
  {{TAGBL|ISYM}} = -1
  {{TAGBL|ISYM}} = -1
  {{TAGBL|NELM}} = 100
  {{TAGBL|NELM}} = 100
Line 115: Line 114:
  {{TAGBL|NSW}} = 10000
  {{TAGBL|NSW}} = 10000
  {{TAGBL|POTIM}} = 3.0
  {{TAGBL|POTIM}} = 3.0
  {{TAGBL|RANDOM_SEED}} =         88951986                0               0
#Parameters for initialization of wavefunctions and charge density
  {{TAGBL|INIWAV}} = 1
{{TAGBL|IWAVPR}} = 1
{{TAGBL|ISTART}} = 0
{{TAGBL|ICHARG}} = 0
   
   
  #Machine learning paramters
  #Machine learning paramters
  {{TAGBL|ML_FF_LMLFF}} = .TRUE.
  {{TAGBL|ML_LMLFF}} = .TRUE.
  {{TAGBL|ML_FF_ISTART}} = 0
  {{TAGBL|ML_ISTART}} = 0
{{TAGBL|ML_FF_NWRITE}} = 2
{{TAGBL|ML_FF_EATOM}} = -.70128086E+00
 
;{{TAG|ML_FF_LMLFF}} = ''.TRUE.'': switches on the machine learning of the force field
;{{TAG|ML_FF_ISTART}} = 0: corresponds to learning from scratch
;{{TAG|ML_FF_NWRITE}} = 2: leads to a more verbose output where the error on energies, forces and stress are written to the {{TAG|ML_LOGFILE}} file. This setting is very handy to check the accuracy of the force field.
;{{TAG|ML_FF_EATOM}} = -.70128086E+00: sets the atomic reference energy for each species. We show to obtain this energy below.
;{{TAG|IWAVPR}} = 1: extrapolates the total charge density from the atomic ones. This is more advisable than the default, because during the on-the-fly learning several hundreds or thousands of molecular dynamics steps can lie between two ab initio calculations.


== Calculation ==
== Calculation ==


=== Reference energy ===
To obtain the force field for liquid Si, we require the atomic energy of a single Si atom.
We create a new working directory for this calculation
  mkdir Si_ATOM
  cd Si_ATOM
  ln -s ../POTCAR
In this directory, we create the following {{TAG|INCAR}} file
#Basic parameters
{{TAG|ISMEAR}} = 0
{{TAG|SIGMA}} = 0.1
{{TAG|LREAL}} = Auto
{{TAG|ALGO}} = FAST
{{TAG|ISYM}} = 0
{{TAG|NELM}} = 100
{{TAG|EDIFF}} = 1E-4
{{TAG|LWAVE}} = .FALSE.
{{TAG|LCHARG}} = .FALSE.
{{TAG|ISPIN}} = 2
The {{TAG|POSCAR}} file contains a single atom in a large enough box (the box should be orthorhombic to have enough degrees of freedom for electronic relaxation)
Si atom
      1.00090000000000
    12.00000000  0.00000000  0.00000000
      0.00000000  12.01000000  0.00000000
      0.00000000  0.00000000  12.02000000
    Si
    1
Direct
    0.00000000  0.00000000  0.00000000
Due to the large unit cell, we can use a Gamma point calculation set up in the {{TAG|KPOINTS}} file
Gamma point only
0 0 0
Gamma
  1 1 1
  0 0 0
After running the calculation, we obtain the total energy from the {{TAG|OUTCAR}} or {{TAG|OSZICAR}} file. We use this energy for the {{TAG|ML_FF_EATOM}} tag. If multiple atom types are present in the structure, this step has to be repeated for each atom type separately and the reference energies are provided as a list after the {{TAG|ML_FF_EATOM}} tag. The ordering of the energies corresponds to the ordering of the elements in the {{TAG|POTCAR}} file.


=== Creating the liquid structure ===
=== Creating the liquid structure ===
Line 178: Line 129:
After running the calculation, we obtained a force field, but its initial trajectory might be tainted but the unreasonable starting position. Nevertheless, it is instructive to inspect the output to understand how to assess the accuracy of a force field, before refining it in subsequent calculations.
After running the calculation, we obtained a force field, but its initial trajectory might be tainted but the unreasonable starting position. Nevertheless, it is instructive to inspect the output to understand how to assess the accuracy of a force field, before refining it in subsequent calculations.
The main output files for the machine learning are
The main output files for the machine learning are
;{{TAG|ML_ABNCAR}}: contains the ab initio structure datasets used for the learning. It will be needed for continuation runs as {{TAG|ML_ABCAR}}.
;{{TAG|ML_ABN}}: contains the ab initio structure datasets used for the learning. It will be needed for continuation runs as {{TAG|ML_AB}}.
;{{TAG|ML_FFNCAR}}: contains the regression results (weights, parameters, etc.). It will be needed for continuation runs as {{TAG|ML_FFCAR}}.
;{{TAG|ML_FFN}}: contains the regression results (weights, parameters, etc.). It will be needed for continuation runs as {{TAG|ML_FF}}.
;{{TAG|ML_LOGFILE}}: logging the proceedings of the machine learning. With {{TAG|ML_FF_NWRITE}} = 2 set, this file lists the error between force-field and ab initio calculation for energy, forces and the stress. The entry for the last step should be similar to the following (the inherent randomness of a molecular dynamics simulation will lead to slightly different results):
;{{TAG|ML_LOGFILE}}: logging the proceedings of the machine learning. This file consists of keywords that are nicely "grepable." The keywords are explained in the in the beginning of the file and upon "grepping". The status of each MD step is given by the keyword "STATUS". Please invoke the following command:
  ====================================================================================================
grep STATUS ML_LOGFILE
      Information on error estimations
The output should look similar to the following:
  ----------------------------------------------------------------------------------------------------
# STATUS ###############################################################
               Error in energy (eV atom^-1):    0.016994
# STATUS This line describes the overall status of each step.
              Error in force (eV Angst^-1):    0.247664
  # STATUS
                      Error in stress (kB):    5.368218
# STATUS nstep ..... MD time step or input structure counter
                Bayesian error (eV Angst-1):    0.064904    0.127195
  # STATUS state ..... One-word description of step action
                        Spilling factor (-):    0.000690    0.020000
# STATUS            - "accurate"  (1) : Errors are low, force field is used
  ====================================================================================================
# STATUS            - "threshold" (2) : Errors exceeded threshold, structure is sampled from ab initio
You can see that additional information about the Bayesian error and the spilling factor is reported.
# STATUS            - "learning"  (3) : Stored configurations are used for training force field
The first entry of the Bayesian error is the estimated error from our model. If the estimated error exceeds the threshold listed next to it a new structure dataset is generated. The threshold is automatically determined during the calculations. The first and second entry for the spilling factor are the calculated spilling factor and the threshold, respectively. The threshold for the spilling factor is usually always kept constant during the calculations.
# STATUS            - "critical"  (4) : Errors are high, ab initio sampling and learning is enforced
# STATUS            - "predict"  (5) : Force field is used in prediction mode only, no error checking
# STATUS is ........ Integer representation of above one-word description (integer in parenthesis)
# STATUS doabin .... Perform ab initio calculation (T/F)
# STATUS iff ....... Force field available (T/F, False after startup hints to possible convergence problems)
# STATUS nsample ... Number of steps since last reference structure collection (sample = T)
# STATUS ngenff .... Number of steps since last force field generation (genff = T)
# STATUS ###############################################################
# STATUS            nstep    state is doabin    iff  nsample    ngenff
# STATUS               2        3  4      5      6        7        8
# STATUS ###############################################################
STATUS                  0 threshold  2      T      F        0        0
STATUS                  1 critical  4      T      F        0        1
STATUS                  2 critical  4      T      F        0        2
STATUS                  3 critical  4      T      T        0        1
STATUS                  4 critical  4      T      T        0        1
STATUS                  5 critical  4      T      T        0        1
      .                  .        .  .      .      .        .        .
      .                  .        .  .      .      .        .        .
      .                  .        .  .      .      .        .        .
STATUS              9997 accurate  1      F      T      945      996
STATUS              9998 accurate  1      F      T      946      997
STATUS              9999 accurate  1      F      T      947      998
STATUS              10000 learning  3      T      T      948      999
 
Another important keyword is "ERR". For this instance we should type the following command:
grep ERR ML_LOGFILE
The output should look like the following:
# ERR ######################################################################
# ERR This line contains the RMSEs of the predictions with respect to ab initio results for the training data.
# ERR
# ERR nstep ......... MD time step or input structure counter
# ERR rmse_energy ... RMSE of energies (eV atom^-1)
  # ERR rmse_force .... RMSE of forces (eV Angst^-1)
# ERR rmse_stress ... RMSE of stress (kB)
# ERR ######################################################################
# ERR              nstep      rmse_energy      rmse_force      rmse_stress
# ERR                  2                3                4                5
# ERR ######################################################################
ERR                    0  0.00000000E+00  0.00000000E+00  0.00000000E+00
ERR                    1   0.00000000E+00  0.00000000E+00  0.00000000E+00
ERR                    2  0.00000000E+00  0.00000000E+00  0.00000000E+00
  ERR                    3  2.84605192E-05  9.82351889E-03  2.40003743E-02
ERR                    4  1.83193349E-05  1.06700600E-02  5.37606479E-02
ERR                    5  4.12132223E-05  1.34123085E-02  1.01588957E-01
ERR                    6  9.51627413E-05  1.90335214E-02  1.31959103E-01
.                      .                .                .                .
.                      .                .                .                .
.                      .                .                .                .
ERR                  9042  1.07159240E-02  2.41283323E-01  4.95695745E+00
ERR                  9052  1.07159240E-02  2.41283323E-01  4.95695745E+00
ERR                10000  1.07159240E-02  2.41283323E-01  4.95695745E+00
 
This tag lists the errors on the energy, forces and stress of the force field compared to the ab initio results on the available training data. The second column shows the MD step. We see that the entry is not output at every MD step. The errors only change if the force field is updated, hence when an ab initio calculation is executed (it should correlate with the doabin column of the STATUS keyword). The other three columns show the errors on the energy (eV/atom), forces (ev/Angstrom) and stress (kB).


=== Structral properties of the force field ===
=== Structral properties of the force field ===
Next we will look at the accuracy of structural properties of the force field. For the we first run a 3000 fs molecular dynamics calculation with and without the force field starting from the new {{TAG|CONTCAR}} file. First we save that new {{TAG|CONTCAR}} file to
To examine the accuracy of structural properties, we compare the deviations between a 3 ps molecular dynamics run using the force field and a full ab initio calculation. For a meaningful comparison, it is best to start from the same initial structure. We will use the liquid structure, we obtained in the previous step and back it up
  cp CONTCAR POSCAR.T2000_relaxed
  cp CONTCAR POSCAR.T2000_relaxed
Now do the following steps to run the force field calculation:
*Copy ''POSCAR.T2000_relaxed'' to {{TAG|POSCAR}}.
*Copy {{TAG|ML_ABNCAR}} to {{TAG|ML_ABCAR}}.
*Copy {{TAG|ML_FFNCAR}} to {{TAG|ML_FFCAR}}.
*Change {{TAG|INCAR}} tags:
**Change {{TAG|ML_FF_ISTART}}=0 to {{TAG|ML_FF_ISTART}}=2: This will switch of learning and only use the machine-learned force field.
**Change {{TAG|NSW}}=10000 to {{TAG|NSW}}=1000.
*Run calculation.
*Copy {{TAG|XDATCAR}} to ''XDATCAR.MLFF_3ps''.


Now do the following steps to run the ab initio reference calculation:
Now, we proceed with the force field calculation and set up the required files
*Copy ''POSCAR.T2000_relaxed'' to {{TAG|POSCAR}}.
cp POSCAR.T2000_relaxed {{TAGBL|POSCAR}}
*Change {{TAG|ML_FF_LMLFF}}=''.TRUE.'' to {TAG|ML_FF_LMLFF}}=''.FALSE.'': This will turn off the machine learning completely.
cp {{TAGBL|ML_FFN}} {{TAGBL|ML_FF}}
*Run caclulation.
To run a shorter simulation using only the force field, we change the following {{TAG|INCAR}} tags to
*Copy {{TAG|XDATCAR}} to ''XDATCAR.AI_3ps''.
{{TAGBL|ML_ISTART}} = 2
{{TAGBL|NSW}} = 1000
After the calculation finished, we backup the history of the atomic positions
cp {{TAGBL|XDATCAR}} XDATCAR.MLFF_3ps
To analyze the pair correlation function, we use the PERL script ''pair_correlation_function.pl''
<div class="toccolours mw-customtoggle-script">'''Click to show/hide pair_correlation_function.pl'''</div>
<div class="mw-collapsible mw-collapsed" id="mw-customcollapsible-script">
<pre>
#!/usr/bin/perl


To analyze the pair correlation function use the PERL script ''pair_correlation_function.pl''
use strict;
<div class="toccolours mw-customtoggle-script">'''Click to show/hide pair_correlation_function.pl'''</div>
use warnings;
<div class="mw-collapsible mw-collapsed" id="mw-customcollapsible-script">{{:Pair correlation script}}</div>
 
#configuration for which ensemble average is to be calculated
my $confmin=1;            #starting index of configurations in XDATCAR file for pair correlation function
my $confmax=20000;          #last index of configurations in XDATCAR file for pair correlation function
my $confskip=1;          #stepsize for configuration loop
my $species_1 = 1;        #species 1 for which pair correlation function is going to be calculated
my $species_2 = 1;        #species 2 for which pair correlation function is going to be calculated
#setting radial grid
my $rmin=0.0;            #minimal value of radial grid
my $rmax=10.0;            #maximum value of radial grid
my $nr=300;                #number of equidistant steps in radial grid
my $dr=($rmax-$rmin)/$nr; #stepsize in radial grid
my $tol=0.0000000001;    #tolerance limit for r->0
my $z=0;                  #counter
my $numelem;              #number of elements
my @elements;            #number of atoms per element saved in list/array
my $lattscale;            #scaling factor for lattice
my @b;                    #Bravais matrix
my $nconf=0;              #number of configurations in XDATCAR file
my @cart;                #Cartesian coordinates for each atom and configuration
my $atmin_1=0;            #first index of species one
my $atmax_1;              #last index of species one
my $atmin_2=0;            #first index of species two
my $atmax_2;              #last index of species two
my @vol;                  #volume of cell (determinant of Bravais matrix)
my $pi=4*atan2(1, 1);    #constant pi
my $natom=0;              #total number of atoms in cell
my @pcf;                  #pair correlation function (list/array)
my $mult_x=1;            #periodic repetition of cells in x dimension
my $mult_y=1;            #periodic repetition of cells in y dimension
my $mult_z=1;            #periodic repetition of cells in z dimension
my @cart_super;          #Cartesian cells over multiple cells
my @vec_len;              #Length of lattice vectors in 3 spatial coordinates
#my $ensemble_type="NpT";  #Set Npt or NVT. Needs to be set since both have different XDATCAR file.
my $ensemble_type="NVT";  #Set Npt or NVT. Needs to be set since both have different XDATCAR file.
my $av_vol=0;              #Average volume in cell
 
#reading in XDATCAR file
while (<>)
{
  chomp;
  $_=~s/^/ /;
  my @help=split(/[\t,\s]+/);
  $z++;
  if ($z==2)
  {
      $lattscale = $help[1];
  }
  if ($z==3)
  {
      $b[$nconf+1][1][1]=$help[1]*$lattscale;
      $b[$nconf+1][1][2]=$help[2]*$lattscale;
      $b[$nconf+1][1][3]=$help[3]*$lattscale;
  }
  if ($z==4)
  {
      $b[$nconf+1][2][1]=$help[1]*$lattscale;
      $b[$nconf+1][2][2]=$help[2]*$lattscale;
      $b[$nconf+1][2][3]=$help[3]*$lattscale;
  }
  if ($z==5)
  {
      $b[$nconf+1][3][1]=$help[1]*$lattscale;
      $b[$nconf+1][3][2]=$help[2]*$lattscale;
      $b[$nconf+1][3][3]=$help[3]*$lattscale;
  }
  if ($z==7)
  {
      if ($nconf==0)
      {
        $numelem=@help-1;
        for (my $i=1;$i<=$numelem;$i++)
        {
            $elements[$i]=$help[$i];
            $natom=$natom+$help[$i];
        }
      }
  }
  if ($_=~m/Direct/)
  {
      $nconf=$nconf+1;
      #for NVT ensemble only one Bravais matrix exists, so it has to be copied
      if ($ensemble_type eq "NVT")
      {
        for (my $i=1;$i<=3;$i++)
        {
            for (my $j=1;$j<=3;$j++)
            {
              $b[$nconf][$i][$j]=$b[1][$i][$j];
            }
        }
      }
      for (my $i=1;$i<=$natom;$i++)
      {
        $_=<>;
        chomp;
        $_=~s/^/ /;
        my @helpat=split(/[\t,\s]+/);
        $cart[$nconf][$i][1]=$b[1][1][1]*$helpat[1]+$b[1][1][2]*$helpat[2]+$b[1][1][3]*$helpat[3];
        $cart[$nconf][$i][2]=$b[1][2][1]*$helpat[1]+$b[1][2][2]*$helpat[2]+$b[1][2][3]*$helpat[3];
        $cart[$nconf][$i][3]=$b[1][3][1]*$helpat[1]+$b[1][3][2]*$helpat[2]+$b[1][3][3]*$helpat[3];
      }
      if ($ensemble_type eq "NpT")
      {
        $z=0;
      }
  }
  last if eof;
}
 
if ($confmin>$nconf)
{
  print "Error, confmin larger than number of configurations. Exiting...\n";
  exit;
}
if ($confmax>$nconf)
{
  $confmax=$nconf;
}
for (my $i=1;$i<=$nconf;$i++)
{
  #calculate lattice vector lengths
  $vec_len[$i][1]=($b[$i][1][1]*$b[$i][1][1]+$b[$i][1][2]*$b[$i][1][2]+$b[$i][1][3]*$b[$i][1][3])**0.5;
  $vec_len[$i][2]=($b[$i][2][1]*$b[$i][2][1]+$b[$i][2][2]*$b[$i][2][2]+$b[$i][2][3]*$b[$i][2][3])**0.5;
  $vec_len[$i][3]=($b[$i][3][1]*$b[$i][3][1]+$b[$i][3][2]*$b[$i][3][2]+$b[$i][3][3]*$b[$i][3][3])**0.5;
  #calculate volume of cell
  $vol[$i]=$b[$i][1][1]*$b[$i][2][2]*$b[$i][3][3]+$b[$i][1][2]*$b[$i][2][3]*$b[$i][3][1]+$b[$i][1][3]*$b[$i][2][1]*$b[$i][3][2]-$b[$i][3][1]*$b[$i][2][2]*$b[$i][1][3]-$b[$i][3][2]*$b[$i][2][3]*$b[$i][1][1]-$b[$i][3][3]*$b[$i][2][1]*$b[$i][1][2];
  $av_vol=$av_vol+$vol[$i];
}
$av_vol=$av_vol/$nconf;


Obtain the pair correlation function from the previously saved {{TAG|XDATCAR}} files:
#choose species 1 for which pair correlation function is going to be calculated
$atmin_1=1;
if ($species_1>1)
{
  for (my $i=1;$i<$species_1;$i++)
  {
    $atmin_1=$atmin_1+$elements[$i];
  }
}
$atmax_1=$atmin_1+$elements[$species_1]-1;
#choose species 2 to which paircorrelation function is calculated to
$atmin_2=1;
if ($species_2>1)
{
  for (my $i=1;$i<$species_2;$i++)
  {
    $atmin_2=$atmin_2+$elements[$i];
  }
}
$atmax_2=$atmin_2+$elements[$species_2]-1;
#initialize pair correlation function
for (my $i=0;$i<=($nr-1);$i++)
{
  $pcf[$i]=0.0;
}
# loop over configurations, make histogram of pair correlation function  
for (my $j=$confmin;$j<=$confmax;$j=$j+$confskip)
{
  for (my $k=$atmin_1;$k<=$atmax_1;$k++)
  {
      for (my $l=$atmin_2;$l<=$atmax_2;$l++)
      {
          if ($k==$l) {next};
          for (my $g_x=-$mult_x;$g_x<=$mult_x;$g_x++)
          {
            for (my $g_y=-$mult_y;$g_y<=$mult_y;$g_y++)
            {
                for (my $g_z=-$mult_y;$g_z<=$mult_z;$g_z++)
                {
                  my $at2_x=$cart[$j][$l][1]+$vec_len[$j][1]*$g_x;
                  my $at2_y=$cart[$j][$l][2]+$vec_len[$j][2]*$g_y;
                  my $at2_z=$cart[$j][$l][3]+$vec_len[$j][3]*$g_z;
                  my $dist=($cart[$j][$k][1]-$at2_x)**2.0+($cart[$j][$k][2]-$at2_y)**2.0+($cart[$j][$k][3]-$at2_z)**2.0;
                  $dist=$dist**0.5;
                  #determine integer multiple
                  my $zz=int(($dist-$rmin)/$dr+0.5);
                  if ($zz<$nr)
                  {
                      $pcf[$zz]=$pcf[$zz]+1.0;
                  }
                }
            }
          }
      }
  }
}
#make ensemble average, rescale functions and print
for (my $i=0;$i<=($nr-1);$i++)
{
  my $r=$rmin+$i*$dr;
  if ($r<$tol)
  {
      $pcf[$i]=0.0;
  }
  else
  {
      $pcf[$i]=$pcf[$i]*$av_vol/(4*$pi*$r*$r*$dr*(($confmax-$confmin)/$confskip)*($atmax_2-$atmin_2+1)*($atmax_1-$atmin_1+1));#*((2.0*$mult_x+1.0)*(2.0*$mult_y+1.0)*(2.0*$mult_z+1.0)));
  }
  print $r," ",$pcf[$i],"\n";
}
</pre>
</div>
and process the previously saved {{TAG|XDATCAR}} files
  perl pair_correlation_function.pl XDATCAR.MLFF_3ps > pair_MLFF_3ps.dat
  perl pair_correlation_function.pl XDATCAR.MLFF_3ps > pair_MLFF_3ps.dat
  perl pair_correlation_function.pl XDATCAR.AI_3ps > pair_AI_3ps.dat
 
Plot the pair correlation functions using the following command:
To save time the pair correlation function for 1000 ab initio MD steps is precalculated in the file ''pair_AI_3ps.dat''.
 
The interested user can of course calculate the results of the ab initio MD by rerunning the above steps while switching off machine learning via
  {{TAGBL|ML_LMLFF}} = .FALSE.
 
We can compare the pair correlation functions, e.g. with gnuplot using the following command
  gnuplot -e "set terminal jpeg; set xlabel 'r(Ang)'; set ylabel 'PCF'; set style data lines; plot 'pair_MLFF_3ps.dat', 'pair_AI_3ps.dat' " > PC_MLFF_vs_AI_3ps.jpg
  gnuplot -e "set terminal jpeg; set xlabel 'r(Ang)'; set ylabel 'PCF'; set style data lines; plot 'pair_MLFF_3ps.dat', 'pair_AI_3ps.dat' " > PC_MLFF_vs_AI_3ps.jpg
The pair correlation functions obtained that way should look similar like that:
The pair correlation functions obtained that way should look similar to this figure


[[File:PC MLFF vs AI 3ps.jpg|400px]]
[[File:PC MLFF vs AI 3ps.jpg|400px]]


We see that pair correlation is quite well reproduced although the error on the force is a little bit large. Nevertheless we just used this learning to very create a liquid structure very fast, since creating a liquid from a perfect crystal can be a time consuming task.
We see that pair correlation is quite well reproduced although the error in the force of ~0.242 eV/<math>\AA</math> shown above is a little bit too large. This error is maybe too large for accurate production calculations (usually an accuracy of approximately 0.1 eV/<math>\AA</math> is targeted), but since the pair correlation function is well reproduced it is perfectly fine to use this on-the-fly force field in the time-consuming melting of the crystal.


=== Obtaining more accurate force field ===
=== Obtaining a more accurate force field ===


Including the melting phase in the force field may impact the accuracy of the force field. To improve it is usually advisable to learn on the pure structures, which in our case this means to use the {{TAG|CONTCAR}} file obtained after the melting. Furthermore, the force field was learned using only a single k point so that we can improve the accuracy of the reference data by including more k points.
Including the melting phase in the force field may impact the accuracy of the force field. To improve it is usually advisable to learn on the pure structures, which in our case this means to use the {{TAG|CONTCAR}} file obtained after the melting. Furthermore, the force field was learned using only a single k point so that we can improve the accuracy of the reference data by including more k points. In most calculations, it is important to conduct accurate ab initio calculations since bad reference data can limit the accuracy or even inhibit the learning of a force field.
After learning how to get a good starting structure for the the liquid we will restart the learning but with better ab initio parameters using the {{TAG|CONTCAR}} from the previous subsection. It is important in most calculations to have quite accurate ab initio calculations since bad ab initio parameters can really limit the accuracy of the learning or even inhibit a proper learning.


We do the following steps to restart learning:
We restart from the liquid structure obtained [[#Creating the liquid structure|before]]
*Copy ''POSCAR.T2000_relaxed'' to {{TAG|POSCAR}}.
cp POSCAR.T2000_relaxed {{TAGBL|POSCAR}}
*Change {{TAG|INCAR}} tags:
and change the following {{TAG|INCAR}} tags
**Set {{TAG|ALGO}}=''Normal''.
{{TAGBL|ALGO}} = Normal
**Set {{TAG|ML_FF_LMLFF}}=''.FALSE.'' back to {{TAG|ML_FF_LMLFF}}=''.TRUE.''.
{{TAGBL|ML_LMLFF}} = .TRUE.
**Set {{TAG|ML_FF_ISTART}}=2 back to {{TAG|ML_FF_ISTART}}=0.
{{TAGBL|ML_ISTART}} = 0
**Set {{TAG|NSW}}=1000 back to {{TAG|NSW}}=10000. We will learn again for 30 ps.
{{TAGBL|NSW}} = 1000
**Optionally add k point parallelization wth the {{TAG|KPAR}} tag (if you are not familiar with this tag leave it be).
If you run have resources to run in parallel, you can reduce the computation time further by adding k point parallelization with the {{TAG|KPAR}} tag.
*Increase the k mesh in the {{TAG|KPOINTS}} file. The new file looks like:
We use a denser k-point mesh in the {{TAG|KPOINTS}} file
  test
  2x2x2 Gamma-centered mesh
  0 0 0
  0 0 0
  Gamma
  Gamma
   2 2 2
   2 2 2
   0 0 0
   0 0 0
*Run the calculation using the '''standard''' version of VASP (usually ''vasp_std'').
We will learn a new force field with 1000 MD steps (each of 3 fs). Keep in mind to run the calculation using the '''standard''' version of VASP (usually ''vasp_std'').
After running the calculation, we examine the error "ERR" in the {{TAG|ML_LOGFILE}} by typing:
grep "ERR" ML_LOGFILE
where the last entries should be close to
ERR                  886  5.98467749E-03  1.48190308E-01  2.38264786E+00
ERR                  908  5.98467749E-03  1.48190308E-01  2.38264786E+00
ERR                  925  5.98467749E-03  1.48190308E-01  2.38264786E+00
ERR                  959  5.98467749E-03  1.48190308E-01  2.38264786E+00
ERR                  980  5.98467749E-03  1.48190308E-01  2.38264786E+00
ERR                  990  5.99559653E-03  1.50261779E-01  2.40349561E+00
ERR                  1000  5.99559653E-03  1.50261779E-01  2.40349561E+00


After running the calculation we will again look at the error in the {{TAG|ML_LOGFILE}}, where the obtained values should be close to:
We immediately see that the errors for the forces are significantly lower than in the previous calculation with only one k point. This is due to the less noisy ab initio data which is easier to learn.
====================================================================================================
      Information on error estimations
----------------------------------------------------------------------------------------------------
              Error in energy (eV atom^-1):    0.006079
              Error in force  (eV Angst^-1):    0.165678
                      Error in stress (kB):    2.553148
                Bayesian error (eV Angst-1):    0.044695    0.101357
                        Spilling factor (-):    0.002285    0.020000
====================================================================================================


We immediately see that the errors are significantly lower than in the training with one k point.
To understand how the force field is learned, we inspect the {{TAG|ML_ABN}} file containing the training data. In the beginning of this file, you will find information about the number of reference structures for training
 
  1.0 Version
Next we try to look at how well the force field is learned and try to monitor how much training data is added to a structure after each learning cycle.
 
The {{TAG|ML_ABNCAR}} file contains the training data. The number of reference structures and the basis-set size are written at the beginning of this file. The values in this example at this step should be close to the following:
  **************************************************
  **************************************************
       The number of configurations
       The number of configurations
  --------------------------------------------------
  --------------------------------------------------
           55
           48
and
and the number of local reference configurations (size of the basis set)
  **************************************************
  **************************************************
       The numbers of basis sets per atom type
       The numbers of basis sets per atom type
  --------------------------------------------------
  --------------------------------------------------
         396
         382
for the training-set size and basis-set size, respectively.


Now we want to further continue the learning to see how much data is added and how the accuracy of the force field changes.
We will further continue the training a 1000 MD steps and see how the number of training structures and the number of local reference configurations change.  
Do the following steps for a continuation run with on-the-fly learning:
Do the following steps:
*Copy {{TAG|ML_ABNCAR}} to {{TAG|ML_ABCAR}}.
cp {{TAGBL|ML_ABN}} {{TAGBL|ML_AB}}
*Copy {{TAG|CONTCAR}} to {{TAG|POSCAR}}.
cp {{TAGBL|CONTCAR}} {{TAGBL|POSCAR}}
*Modify the {{TAG|INCAR}} file in the following way:
and set the following {{TAG|INCAR}} tag
**Change {{TAG|ML_FF_ISTART}}=0 to {{TAG|ML_FF_ISTART}}=1.
{{TAGBL|ML_ISTART}} = 1
**Add {{TAG|ML_FF_CTIFOR}}=''x'', where ''x'' is the last criterion for the threshold of the Bayesian error obtained from the {{TAG|ML_LOGFILE}} (from the entry above it would be 0.101357, but please use the value that you obtained in your calculation). By setting already a good guess for the threshold several ab initio steps can be skipped from the beginning which would be required to determine the Bayesian error criterion. '''Mind''': When continuation runs are performed where the crystal structure is changed, the last obtained error criterion for the Bayesian error can be too large and then many ab initio steps can be skipped wrongly (this can especially happen when going from first a liquid to a solid phase). In that case it is safer to use the default of {{TAG|ML_FF_CTIFOR}}=10^{-16}</math>}}.


After learning we again look at the last entry of the errors in the {{TAG|ML_LOGFILE}}, which should look like:
After running the calculation, we inspect the last instance of the errors in the {{TAG|ML_LOGFILE}} by typing:
  ====================================================================================================
  grep ERR ML_LOGFILE
      Information on error estimations
The last few lines should have values close to:
  ----------------------------------------------------------------------------------------------------
  ERR                  675  5.10937061E-03  1.46895065E-01  2.50094941E+00
              Error in energy (eV atom^-1):    0.006252
ERR                  861  5.10937061E-03  1.46895065E-01  2.50094941E+00
              Error in force (eV Angst^-1):    0.168549
  ERR                  924  5.10937061E-03  1.46895065E-01  2.50094941E+00
                      Error in stress (kB):    2.606332
ERR                  942  5.01460989E-03  1.47816836E-01  2.47329693E+00
                Bayesian error (eV Angst-1):    0.037569    0.101357
ERR                  1000  5.01460989E-03  1.47816836E-01  2.47329693E+00
                        Spilling factor (-):    0.001114    0.020000
We see that the accuracy has changed slightly. We also look at the ML_ABN file and the number of reference structures for training should increase compared to the run before
====================================================================================================
  1.0 Version
We see that the accuracy has not significantly changed. Also the threshold for the Bayesian error has not change (by typing ''grep "Bayesian error (eV Angst-1):" ML_LOGFILE we see that it was the same the whole time). By looking at the actual errors we see that that the threshold was overwritten only very few times. To see how often the threshold was overstepped type the following command on the command line:
grep "Bayesian error (eV Angst-1):" ML_LOGFILE |awk '{if ($5>0.101357) {print $5}}'
Consequently only very few structures were added to the ab initio data in the {{TAG|ML_ABNCAR}} file. The entry for the reference structures and basis sets should look like:
  **************************************************
  **************************************************
       The number of configurations
       The number of configurations
  --------------------------------------------------
  --------------------------------------------------
           65
           99
and
Also the number of local reference configurations (basis sets) increases compared to the previous calculation
  **************************************************
  **************************************************
       The numbers of basis sets per atom type
       The numbers of basis sets per atom type
  --------------------------------------------------
  --------------------------------------------------
         407
         665
This means that either the force field is already accurate enough or that the threshold is set too high and ab initio calculations are skipped because the force field is too accurate for that criterion.
 
Next we will test the learning with the default value for {{TAG|ML_FF_CTIFOR}} (which is practically zero), to see if the force field changes significantly. For that the following steps have to be done:
*Copy {{TAG|CONTCAR}} to {{TAG|POSCAR}}.
*Copy {{TAG|ML_ABNCAR}} to {{TAG|ML_ABCAR}}.
*Remove the {{TAG|ML_FF_ISTART}} line in the {{TAG|INCAR}} file.
 
After rerunning the calculation we see that many structures have been added in the {{TAG|ML_ABNCAR}} file:
  **************************************************
      The number of configurations
--------------------------------------------------
        160
...
**************************************************
      The numbers of basis sets per atom type
--------------------------------------------------
        726
Nevertheless the accuracy of the force field did not improve significantly in the {{TAG|ML_LOGFILE}}:
====================================================================================================
      Information on error estimations
----------------------------------------------------------------------------------------------------
              Error in energy (eV atom^-1):    0.005950
              Error in force  (eV Angst^-1):    0.162064
                      Error in stress (kB):    2.628567
                Bayesian error (eV Angst-1):    0.029723    0.061348
                        Spilling factor (-):    0.000406    0.020000
====================================================================================================
 
Ideally one should continue learning until no structures need to be added to the training data and basis set any more. Practically this is hard to achieve, since the prediction of the Bayesian error has some inaccuracies and learning can be executed even if the force field is accurate enough. Nevertheless for practical calculations one has to stop the calculation at a given point. In this example we stop here because the accuracy on test structures compared to the ab initio data is not improving in the {{TAG|ML_LOGFILE}} by further addition of training data and this example is only a tutorial example. For high quality production calculations suited for publications we would advise to learn until almost no data is added to the training structures and basis set. In many cases this involves a length of 100 ps for learning.
 
'''User task''': The user should continue learning and observe the convergence of the number of reference structures. Most likely the maximum allowed basis set size {{TAG|ML_FF_MB_MB}} needs to be increased. The user should also look at the pair correlation function with respect to the ab initio data. Is it still ok?
 
=== Optimizing weights on energy, forces and stress ===
 
Next we learn how to refine an existing force after it has been learned. We will use the force field already obtained from the previous subsection. In this wiki we show the results for the force field learned for 30 ps. If one has learned for a longer time (user task in the previous example) one can also use that force field, but should of course expect larger deviations from the results presented here. The input parameters {{TAG|ML_FF_WTOTEN}}, {{TAG|ML_FF_WTIFOR}} and {{TAG|ML_FF_WTSIF}} are the weights for energy, force and stress in the linear fitting, respectively. By default they are set to 1.0, so energy, force and stress are weighted equally. The weights are relative weights, this means it is sufficient to increase only the weight of interest, while keeping the other parameters at 1.0. If the weight for a component is increased the error of that component usually becomes lower with higher weight. Simultaneously the error on the other components gets worse, but at a different rate. For a given optimal value of the weight the overall error can be minimized, such that the component of interest is reproduced with much more accuracy. Usually this can be important for methods where the free energy is important (such as e.g. {{TAG|Interface pinning calculations}}, {{TAG|Metadynamics}} etc.) and this weighting can improve the error on energy significantly while only slightly making the errors on forces and stress worse.
 
To run the weight optimizations calculations the following steps are necessary:
*Copy {{TAG|INCAR}} to ''INCAR.run''. This will serve as a template since several short calculations will be executed with different weights for the energy.
*Copy {{TAG|CONTCAR}} to ''POSCAR.run''. For each calculation the same {{TAG|POSCAR}} file needs to be used as a starting point.
*Copy {{TAG|ML_ABNCAR}} to {{TAG|ML_ABCAR}}.
*Modify ''INCAR.run'' as follows:
**Set {{TAG|ML_FF_ISTART}}=1.
**Set {{TAG|NSW}}=1. We will only need a single new test structure.
**The {{TAG|ML_FF_WTOTEN}} test will be added by the runscript.
*Use the bash script ''run_param_opt.sh'' (or use your own) which contains the following lines:
#!/bin/bash
if [ -f error_vs_eweight.dat ]; then
    rm error_vs_eweight.dat
fi
touch error_vs_eweight.dat
for i in 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
do
    cat INCAR.run > INCAR
    echo "ML_FF_WTOTEN = $i" >> INCAR
    cp POSCAR.run POSCAR
    mpirun -np $np $executable_path/vasp_std
    cp ML_LOGFILE ML_LOGFILE.$i
    awk -v var="$i" '/Error in energy \(eV atom\^-1\)\:/{energy=$6} /Error in force  \(eV Angst\^-1\)\:/ {force=$6} /Error in stress \(kB\)\:/{stress=$5} /Energy SD. \(eV atom\^-1\)\:/{energy_var=$5}  /Force SD. \(eV Angst\^-1\)\:/{force_var=$5} /Stress SD. \(kB\)\:/ {stress_var=$4} END{print var,energy/energy_var+force/force_var+stress/stress_var}' ML_LOGFILE.$i >> error_vs_eweight.dat
done
The user needs to set the executable path ''$executable_path'' in this directory (and maybe the executable name).
This script adds the weight for the energy ''ML_FF_WTOTEN = $i'' to the {{TAG|INCAR}} file, where ''$i'' runs from 1.0 to 5.0 with a step size of 0.5. After that a single step calculation is performed. The errors compared to ab initio and standard deviations for energies, forces and stress are output to the {{TAG|ML_LOGFILE}} and from that file the sum of the normalized errors is plotted against the weight of the energy. The normalization is done by dividing the errors by the standard deviation for each component.
 
The normalized total error vs. weight for the energy is plotted from the file ''error_vs_eweight.dat'':
gnuplot -e "set terminal jpeg; set xlabel 'Energy weight'; set ylabel 'Error'; set style data lines; plot 'error_vs_eweight.dat'" > error_vs_eweight.jpg
It should look similar to:
 
[[File:Error vs eweight.jpg|400px]]
 
From this plot we see that an ideal value is around  {{TAG|ML_FF_WTOTEN}}=2.5. Next we take a look at the actual errors for that weighting parameter in the file ''ML_LOGFILE.2.5'':
====================================================================================================
      Information on error estimations
----------------------------------------------------------------------------------------------------
              Error in energy (eV atom^-1):    0.005322
              Error in force  (eV Angst^-1):    0.169356
                      Error in stress (kB):    2.849817
                Bayesian error (eV Angst-1):    0.025548    0.000000
                        Spilling factor (-):    0.000368    0.020000
====================================================================================================
We see that the error on energy decreased while it increased for forces and stress compared to ''ML_LOGFILE.1.0'':
====================================================================================================
      Information on error estimations
----------------------------------------------------------------------------------------------------
              Error in energy (eV atom^-1):    0.005954
              Error in force  (eV Angst^-1):    0.162006
                      Error in stress (kB):    2.628128
                Bayesian error (eV Angst-1):    0.028542    0.000000
                        Spilling factor (-):    0.000368    0.020000
====================================================================================================
In this example the decrease of the energy is only small but in many examples this can lead to signifcant increase in the precision.
 
Here are the errors for ''ML_LOGFILE_5.0'':
====================================================================================================
      Information on error estimations
----------------------------------------------------------------------------------------------------
              Error in energy (eV atom^-1):    0.004692
              Error in force  (eV Angst^-1):    0.179448
                      Error in stress (kB):    3.180711
                Bayesian error (eV Angst-1):    0.023225    0.000000
                        Spilling factor (-):    0.000368    0.020000
====================================================================================================
We see that for higher values of the weight the energy is still getting more accurate but the forces and stress are getting loosing accuracy at a higher, such that the overall error gets worse. We advise to use the optimal value.
 
If needed this waiting can be done in a similar way for the forces and stress.
 
=== Production runs using the optimized parameters ===


The production runs using the optimized parameters are run as normal calculations only using an additional line for the weight on energy. We repeat here which steps have to be performed:
*Copy {{TAG|CONTCAR}} to {{TAG|POSCAR}} if needed.
*Copy {{TAG|ML_ABNCAR}} to {{TAG|ML_ABCAR}}.
*Copy {{TAG|ML_FFNCAR}} to {{TAG|ML_FFCAR}}.
*Modify {{TAG|INCAR}} file:
**Set {{TAG|ML_FF_ISTART}}=2.
**Set {{TAG|ML_FF_WTOTEN}}=2.5.
**Set necessary molecular dynamics parameters (like e.g. {{TAG|NSW}} etc.).


'''User task''': Calculate the pair correlation function as above with and without machine learning starting from the same {{TAG|POSCAR}} and compare the results.
Ideally, one should continue learning until no structures need to be added to the training data and basis set anymore. Very often this can take up to 100ps depending on the material and conditions. In practice, the prediction of the Bayesian error exhibits numerical inaccuracies so that an ab initio calculation is conducted from time to time even if the force field is accurate enough. So measuring only the decreasing frequency of addition of new data is not sufficient to know when to finish. One should also look at the accuracy of the force on the training data and more importantly on the accuracy on some test data that is outside of the training sets.


== Download ==
== Download ==
[http://www.vasp.at/vasp-workshop/examples/Liquid_Si_MLFF.tgz Liquid_Si_MLFF.tgz]
[[Media:MLFF_Liquid_Si_tutorial.tgz| MLFF_Liquid_Si_tutorial.tgz]]


{{Template:Machine learning force field - Tutorial}}
{{Template:Machine learning force field - Tutorial}}


[[Category:Examples]]
[[Category:Examples]]

Latest revision as of 15:13, 15 April 2022

Task

Generating a machine learning force field for liquid Si. For this tutorial, we expect that the user is already familiar with running conventional ab initio molecular dynamic calculations.

Input

POSCAR

In this example we start from a 64 atom super cell of diamond-fcc Si (the same as in Liquid Si - Standard MD): 

Si_CD_2x2x2
     5.43090000000000
    2.00000000   0.00000000   0.00000000
    0.00000000   2.00000000   0.00000000
    0.00000000   0.00000000   2.00000000
   Si
   64
Direct
   0.00000000   0.00000000   0.00000000
   0.50000000   0.00000000   0.00000000
   0.00000000   0.50000000   0.00000000
   0.50000000   0.50000000   0.00000000
   0.00000000   0.00000000   0.50000000
   0.50000000   0.00000000   0.50000000
   0.00000000   0.50000000   0.50000000
   0.50000000   0.50000000   0.50000000
   0.37500000   0.12500000   0.37500000
   0.87500000   0.12500000   0.37500000
   0.37500000   0.62500000   0.37500000
   0.87500000   0.62500000   0.37500000
   0.37500000   0.12500000   0.87500000
   0.87500000   0.12500000   0.87500000
   0.37500000   0.62500000   0.87500000
   0.87500000   0.62500000   0.87500000
   0.00000000   0.25000000   0.25000000
   0.50000000   0.25000000   0.25000000
   0.00000000   0.75000000   0.25000000
   0.50000000   0.75000000   0.25000000
   0.00000000   0.25000000   0.75000000
   0.50000000   0.25000000   0.75000000
   0.00000000   0.75000000   0.75000000
   0.50000000   0.75000000   0.75000000
   0.37500000   0.37500000   0.12500000
   0.87500000   0.37500000   0.12500000
   0.37500000   0.87500000   0.12500000
   0.87500000   0.87500000   0.12500000
   0.37500000   0.37500000   0.62500000
   0.87500000   0.37500000   0.62500000
   0.37500000   0.87500000   0.62500000
   0.87500000   0.87500000   0.62500000
   0.25000000   0.00000000   0.25000000
   0.75000000   0.00000000   0.25000000
   0.25000000   0.50000000   0.25000000
   0.75000000   0.50000000   0.25000000
   0.25000000   0.00000000   0.75000000
   0.75000000   0.00000000   0.75000000 
   0.25000000   0.50000000   0.75000000
   0.75000000   0.50000000   0.75000000
   0.12500000   0.12500000   0.12500000
   0.62500000   0.12500000   0.12500000
   0.12500000   0.62500000   0.12500000
   0.62500000   0.62500000   0.12500000
   0.12500000   0.12500000   0.62500000
   0.62500000   0.12500000   0.62500000
   0.12500000   0.62500000   0.62500000
   0.62500000   0.62500000   0.62500000
   0.25000000   0.25000000   0.00000000
   0.75000000   0.25000000   0.00000000
   0.25000000   0.75000000   0.00000000
   0.75000000   0.75000000   0.00000000
   0.25000000   0.25000000   0.50000000
   0.75000000   0.25000000   0.50000000
   0.25000000   0.75000000   0.50000000
   0.75000000   0.75000000   0.50000000
   0.12500000   0.37500000   0.37500000
   0.62500000   0.37500000   0.37500000
   0.12500000   0.87500000   0.37500000
   0.62500000   0.87500000   0.37500000
   0.12500000   0.37500000   0.87500000
   0.62500000   0.37500000   0.87500000
   0.12500000   0.87500000   0.87500000
   0.62500000   0.87500000   0.87500000

KPOINTS

We will start with a single k point in this example: 

K-Points
 0
Gamma
 1  1  1
 0  0  0

INCAR

#Basic parameters
ISMEAR = 0
SIGMA = 0.1
LREAL = Auto
ISYM = -1
NELM = 100
EDIFF = 1E-4
LWAVE = .FALSE.
LCHARG = .FALSE.

#Parallelization of ab initio calculations
NCORE = 2

#MD
IBRION = 0
MDALGO = 2
ISIF = 2
SMASS = 1.0
TEBEG = 2000
NSW = 10000
POTIM = 3.0
RANDOM_SEED =          88951986                0                0

#Machine learning paramters
ML_LMLFF = .TRUE.
ML_ISTART = 0

Calculation

Creating the liquid structure

Because we don't have a structure of liquid silicon readily available, we first create that structure by starting from a super cell of crystalline silicon with 64 atoms. The temperature is set to 2000 K so that the crystal melts rapidly in the MD run. To improve the simulation speed drastically, we utilize the on-the-fly machine learning. Most of the ab initio steps will be replaced by very fast force-field ones. Within 10000 steps equivalent to 30 ps, we have obtained a good starting position for the subsequent simulations in the CONTCAR file. You can copy the input files or download them.

After running the calculation, we obtained a force field, but its initial trajectory might be tainted but the unreasonable starting position. Nevertheless, it is instructive to inspect the output to understand how to assess the accuracy of a force field, before refining it in subsequent calculations. The main output files for the machine learning are

ML_ABN
contains the ab initio structure datasets used for the learning. It will be needed for continuation runs as ML_AB.
ML_FFN
contains the regression results (weights, parameters, etc.). It will be needed for continuation runs as ML_FF.
ML_LOGFILE
logging the proceedings of the machine learning. This file consists of keywords that are nicely "grepable." The keywords are explained in the in the beginning of the file and upon "grepping". The status of each MD step is given by the keyword "STATUS". Please invoke the following command:
grep STATUS ML_LOGFILE

The output should look similar to the following: 

# STATUS ###############################################################
# STATUS This line describes the overall status of each step.
# STATUS 
# STATUS nstep ..... MD time step or input structure counter
# STATUS state ..... One-word description of step action
# STATUS             - "accurate"  (1) : Errors are low, force field is used
# STATUS             - "threshold" (2) : Errors exceeded threshold, structure is sampled from ab initio
# STATUS             - "learning"  (3) : Stored configurations are used for training force field
# STATUS             - "critical"  (4) : Errors are high, ab initio sampling and learning is enforced
# STATUS             - "predict"   (5) : Force field is used in prediction mode only, no error checking
# STATUS is ........ Integer representation of above one-word description (integer in parenthesis)
# STATUS doabin .... Perform ab initio calculation (T/F)
# STATUS iff ....... Force field available (T/F, False after startup hints to possible convergence problems)
# STATUS nsample ... Number of steps since last reference structure collection (sample = T)
# STATUS ngenff .... Number of steps since last force field generation (genff = T)
# STATUS ###############################################################
# STATUS            nstep     state is doabin    iff   nsample    ngenff
# STATUS                2         3  4      5      6         7         8
# STATUS ###############################################################
STATUS                  0 threshold  2      T      F         0         0
STATUS                  1 critical   4      T      F         0         1
STATUS                  2 critical   4      T      F         0         2
STATUS                  3 critical   4      T      T         0         1
STATUS                  4 critical   4      T      T         0         1
STATUS                  5 critical   4      T      T         0         1
     .                  .        .   .      .      .         .         .
     .                  .        .   .      .      .         .         .
     .                  .        .   .      .      .         .         .
STATUS               9997 accurate   1      F      T       945       996
STATUS               9998 accurate   1      F      T       946       997
STATUS               9999 accurate   1      F      T       947       998
STATUS              10000 learning   3      T      T       948       999

Another important keyword is "ERR". For this instance we should type the following command: 

grep ERR ML_LOGFILE

The output should look like the following: 

# ERR ######################################################################
# ERR This line contains the RMSEs of the predictions with respect to ab initio results for the training data.
# ERR 
# ERR nstep ......... MD time step or input structure counter
# ERR rmse_energy ... RMSE of energies (eV atom^-1)
# ERR rmse_force .... RMSE of forces (eV Angst^-1)
# ERR rmse_stress ... RMSE of stress (kB)
# ERR ######################################################################
# ERR               nstep      rmse_energy       rmse_force      rmse_stress
# ERR                   2                3                4                5
# ERR ######################################################################
ERR                     0   0.00000000E+00   0.00000000E+00   0.00000000E+00
ERR                     1   0.00000000E+00   0.00000000E+00   0.00000000E+00
ERR                     2   0.00000000E+00   0.00000000E+00   0.00000000E+00
ERR                     3   2.84605192E-05   9.82351889E-03   2.40003743E-02
ERR                     4   1.83193349E-05   1.06700600E-02   5.37606479E-02
ERR                     5   4.12132223E-05   1.34123085E-02   1.01588957E-01
ERR                     6   9.51627413E-05   1.90335214E-02   1.31959103E-01
.                       .                .                .                .
.                       .                .                .                .
.                       .                .                .                .
ERR                  9042   1.07159240E-02   2.41283323E-01   4.95695745E+00
ERR                  9052   1.07159240E-02   2.41283323E-01   4.95695745E+00
ERR                 10000   1.07159240E-02   2.41283323E-01   4.95695745E+00

This tag lists the errors on the energy, forces and stress of the force field compared to the ab initio results on the available training data. The second column shows the MD step. We see that the entry is not output at every MD step. The errors only change if the force field is updated, hence when an ab initio calculation is executed (it should correlate with the doabin column of the STATUS keyword). The other three columns show the errors on the energy (eV/atom), forces (ev/Angstrom) and stress (kB).

Structral properties of the force field

To examine the accuracy of structural properties, we compare the deviations between a 3 ps molecular dynamics run using the force field and a full ab initio calculation. For a meaningful comparison, it is best to start from the same initial structure. We will use the liquid structure, we obtained in the previous step and back it up 

cp CONTCAR POSCAR.T2000_relaxed

Now, we proceed with the force field calculation and set up the required files 

cp POSCAR.T2000_relaxed POSCAR
cp ML_FFN ML_FF

To run a shorter simulation using only the force field, we change the following INCAR tags to 

ML_ISTART = 2
NSW = 1000

After the calculation finished, we backup the history of the atomic positions 

cp XDATCAR XDATCAR.MLFF_3ps

To analyze the pair correlation function, we use the PERL script pair_correlation_function.pl

Click to show/hide pair_correlation_function.pl
#!/usr/bin/perl

use strict;
use warnings;

#configuration for which ensemble average is to be calculated
my $confmin=1;            #starting index of configurations in XDATCAR file for pair correlation function
my $confmax=20000;           #last index of configurations in XDATCAR file for pair correlation function
my $confskip=1;           #stepsize for configuration loop
my $species_1 = 1;        #species 1 for which pair correlation function is going to be calculated
my $species_2 = 1;        #species 2 for which pair correlation function is going to be calculated
#setting radial grid 
my $rmin=0.0;             #minimal value of radial grid
my $rmax=10.0;            #maximum value of radial grid
my $nr=300;                #number of equidistant steps in radial grid
my $dr=($rmax-$rmin)/$nr; #stepsize in radial grid
my $tol=0.0000000001;     #tolerance limit for r->0 
 
my $z=0;                  #counter
my $numelem;              #number of elements
my @elements;             #number of atoms per element saved in list/array
my $lattscale;            #scaling factor for lattice
my @b;                    #Bravais matrix
my $nconf=0;              #number of configurations in XDATCAR file
my @cart;                 #Cartesian coordinates for each atom and configuration
my $atmin_1=0;            #first index of species one
my $atmax_1;              #last index of species one
my $atmin_2=0;            #first index of species two
my $atmax_2;              #last index of species two
my @vol;                  #volume of cell (determinant of Bravais matrix)
my $pi=4*atan2(1, 1);     #constant pi
my $natom=0;              #total number of atoms in cell
my @pcf;                  #pair correlation function (list/array)
my $mult_x=1;             #periodic repetition of cells in x dimension
my $mult_y=1;             #periodic repetition of cells in y dimension
my $mult_z=1;             #periodic repetition of cells in z dimension
my @cart_super;           #Cartesian cells over multiple cells
my @vec_len;              #Length of lattice vectors in 3 spatial coordinates
#my $ensemble_type="NpT";  #Set Npt or NVT. Needs to be set since both have different XDATCAR file.
my $ensemble_type="NVT";  #Set Npt or NVT. Needs to be set since both have different XDATCAR file.
my $av_vol=0;               #Average volume in cell

#reading in XDATCAR file
while (<>)
{
   chomp;
   $_=~s/^/ /;
   my @help=split(/[\t,\s]+/);
   $z++;
   if ($z==2)
   {
      $lattscale = $help[1];
   }
   if ($z==3)
   {
      $b[$nconf+1][1][1]=$help[1]*$lattscale;
      $b[$nconf+1][1][2]=$help[2]*$lattscale;
      $b[$nconf+1][1][3]=$help[3]*$lattscale;
   }
   if ($z==4)
   {
      $b[$nconf+1][2][1]=$help[1]*$lattscale;
      $b[$nconf+1][2][2]=$help[2]*$lattscale;
      $b[$nconf+1][2][3]=$help[3]*$lattscale;
   }
   if ($z==5)
   {
      $b[$nconf+1][3][1]=$help[1]*$lattscale;
      $b[$nconf+1][3][2]=$help[2]*$lattscale;
      $b[$nconf+1][3][3]=$help[3]*$lattscale;
   }
   if ($z==7)
   {
      if ($nconf==0)
      {
         $numelem=@help-1;
         for (my $i=1;$i<=$numelem;$i++)
         {
            $elements[$i]=$help[$i];
            $natom=$natom+$help[$i];
         }
      }
   }
   if ($_=~m/Direct/)
   {
      $nconf=$nconf+1;
      #for NVT ensemble only one Bravais matrix exists, so it has to be copied
      if ($ensemble_type eq "NVT")
      {
         for (my $i=1;$i<=3;$i++)
         { 
            for (my $j=1;$j<=3;$j++)
            {
               $b[$nconf][$i][$j]=$b[1][$i][$j];
            }
         }
      }
      for (my $i=1;$i<=$natom;$i++)
      {
         $_=<>;
         chomp;
         $_=~s/^/ /;
         my @helpat=split(/[\t,\s]+/);
         $cart[$nconf][$i][1]=$b[1][1][1]*$helpat[1]+$b[1][1][2]*$helpat[2]+$b[1][1][3]*$helpat[3];
         $cart[$nconf][$i][2]=$b[1][2][1]*$helpat[1]+$b[1][2][2]*$helpat[2]+$b[1][2][3]*$helpat[3];
         $cart[$nconf][$i][3]=$b[1][3][1]*$helpat[1]+$b[1][3][2]*$helpat[2]+$b[1][3][3]*$helpat[3];
      }
      if ($ensemble_type eq "NpT")
      {
         $z=0;
      }
   } 
   last if eof;
}

if ($confmin>$nconf)
{
   print "Error, confmin larger than number of configurations. Exiting...\n";
   exit;
}
if ($confmax>$nconf)
{
   $confmax=$nconf;
}
 
for (my $i=1;$i<=$nconf;$i++)
{
   #calculate lattice vector lengths
   $vec_len[$i][1]=($b[$i][1][1]*$b[$i][1][1]+$b[$i][1][2]*$b[$i][1][2]+$b[$i][1][3]*$b[$i][1][3])**0.5;
   $vec_len[$i][2]=($b[$i][2][1]*$b[$i][2][1]+$b[$i][2][2]*$b[$i][2][2]+$b[$i][2][3]*$b[$i][2][3])**0.5;
   $vec_len[$i][3]=($b[$i][3][1]*$b[$i][3][1]+$b[$i][3][2]*$b[$i][3][2]+$b[$i][3][3]*$b[$i][3][3])**0.5;
   #calculate volume of cell
   $vol[$i]=$b[$i][1][1]*$b[$i][2][2]*$b[$i][3][3]+$b[$i][1][2]*$b[$i][2][3]*$b[$i][3][1]+$b[$i][1][3]*$b[$i][2][1]*$b[$i][3][2]-$b[$i][3][1]*$b[$i][2][2]*$b[$i][1][3]-$b[$i][3][2]*$b[$i][2][3]*$b[$i][1][1]-$b[$i][3][3]*$b[$i][2][1]*$b[$i][1][2];
   $av_vol=$av_vol+$vol[$i];
}
$av_vol=$av_vol/$nconf;

#choose species 1 for which pair correlation function is going to be calculated
$atmin_1=1;
if ($species_1>1)
{
   for (my $i=1;$i<$species_1;$i++)
   {
     $atmin_1=$atmin_1+$elements[$i];
   }
}
$atmax_1=$atmin_1+$elements[$species_1]-1;
#choose species 2 to which paircorrelation function is calculated to
$atmin_2=1;
if ($species_2>1)
{
   for (my $i=1;$i<$species_2;$i++)
   {
     $atmin_2=$atmin_2+$elements[$i];
   }
}
$atmax_2=$atmin_2+$elements[$species_2]-1;
#initialize pair correlation function
for (my $i=0;$i<=($nr-1);$i++)
{
   $pcf[$i]=0.0;
}
# loop over configurations, make histogram of pair correlation function 
for (my $j=$confmin;$j<=$confmax;$j=$j+$confskip)
{
   for (my $k=$atmin_1;$k<=$atmax_1;$k++)
   {
       for (my $l=$atmin_2;$l<=$atmax_2;$l++)
       {
          if ($k==$l) {next};
          for (my $g_x=-$mult_x;$g_x<=$mult_x;$g_x++)
          {
             for (my $g_y=-$mult_y;$g_y<=$mult_y;$g_y++)
             {
                for (my $g_z=-$mult_y;$g_z<=$mult_z;$g_z++)
                {
                   my $at2_x=$cart[$j][$l][1]+$vec_len[$j][1]*$g_x;
                   my $at2_y=$cart[$j][$l][2]+$vec_len[$j][2]*$g_y;
                   my $at2_z=$cart[$j][$l][3]+$vec_len[$j][3]*$g_z;
                   my $dist=($cart[$j][$k][1]-$at2_x)**2.0+($cart[$j][$k][2]-$at2_y)**2.0+($cart[$j][$k][3]-$at2_z)**2.0;
                   $dist=$dist**0.5;
                   #determine integer multiple 
                   my $zz=int(($dist-$rmin)/$dr+0.5);
                   if ($zz<$nr)
                   {
                      $pcf[$zz]=$pcf[$zz]+1.0;
                   }
                }
             }
          }
       }
   }
}
 
#make ensemble average, rescale functions and print
for (my $i=0;$i<=($nr-1);$i++)
{
   my $r=$rmin+$i*$dr;
   if ($r<$tol)
   {
      $pcf[$i]=0.0;
   }
   else
   {
      $pcf[$i]=$pcf[$i]*$av_vol/(4*$pi*$r*$r*$dr*(($confmax-$confmin)/$confskip)*($atmax_2-$atmin_2+1)*($atmax_1-$atmin_1+1));#*((2.0*$mult_x+1.0)*(2.0*$mult_y+1.0)*(2.0*$mult_z+1.0)));
   }
   print $r," ",$pcf[$i],"\n";
}

and process the previously saved XDATCAR files 

perl pair_correlation_function.pl XDATCAR.MLFF_3ps > pair_MLFF_3ps.dat

To save time the pair correlation function for 1000 ab initio MD steps is precalculated in the file pair_AI_3ps.dat.

The interested user can of course calculate the results of the ab initio MD by rerunning the above steps while switching off machine learning via 

ML_LMLFF = .FALSE.

We can compare the pair correlation functions, e.g. with gnuplot using the following command 

gnuplot -e "set terminal jpeg; set xlabel 'r(Ang)'; set ylabel 'PCF'; set style data lines; plot 'pair_MLFF_3ps.dat', 'pair_AI_3ps.dat' " > PC_MLFF_vs_AI_3ps.jpg

The pair correlation functions obtained that way should look similar to this figure

We see that pair correlation is quite well reproduced although the error in the force of ~0.242 eV/ shown above is a little bit too large. This error is maybe too large for accurate production calculations (usually an accuracy of approximately 0.1 eV/ is targeted), but since the pair correlation function is well reproduced it is perfectly fine to use this on-the-fly force field in the time-consuming melting of the crystal.

Obtaining a more accurate force field

Including the melting phase in the force field may impact the accuracy of the force field. To improve it is usually advisable to learn on the pure structures, which in our case this means to use the CONTCAR file obtained after the melting. Furthermore, the force field was learned using only a single k point so that we can improve the accuracy of the reference data by including more k points. In most calculations, it is important to conduct accurate ab initio calculations since bad reference data can limit the accuracy or even inhibit the learning of a force field.

We restart from the liquid structure obtained before 

cp POSCAR.T2000_relaxed POSCAR

and change the following INCAR tags 

ALGO = Normal
ML_LMLFF = .TRUE.
ML_ISTART = 0
NSW = 1000

If you run have resources to run in parallel, you can reduce the computation time further by adding k point parallelization with the KPAR tag. We use a denser k-point mesh in the KPOINTS file 

2x2x2 Gamma-centered mesh
0 0 0
Gamma
 2 2 2
 0 0 0

We will learn a new force field with 1000 MD steps (each of 3 fs). Keep in mind to run the calculation using the standard version of VASP (usually vasp_std). After running the calculation, we examine the error "ERR" in the ML_LOGFILE by typing: 

grep "ERR" ML_LOGFILE

where the last entries should be close to 

ERR                   886   5.98467749E-03   1.48190308E-01   2.38264786E+00
ERR                   908   5.98467749E-03   1.48190308E-01   2.38264786E+00
ERR                   925   5.98467749E-03   1.48190308E-01   2.38264786E+00
ERR                   959   5.98467749E-03   1.48190308E-01   2.38264786E+00
ERR                   980   5.98467749E-03   1.48190308E-01   2.38264786E+00
ERR                   990   5.99559653E-03   1.50261779E-01   2.40349561E+00
ERR                  1000   5.99559653E-03   1.50261779E-01   2.40349561E+00

We immediately see that the errors for the forces are significantly lower than in the previous calculation with only one k point. This is due to the less noisy ab initio data which is easier to learn.

To understand how the force field is learned, we inspect the ML_ABN file containing the training data. In the beginning of this file, you will find information about the number of reference structures for training 

 1.0 Version
**************************************************
     The number of configurations
--------------------------------------------------
         48

and the number of local reference configurations (size of the basis set) 

**************************************************
     The numbers of basis sets per atom type
--------------------------------------------------
       382

We will further continue the training a 1000 MD steps and see how the number of training structures and the number of local reference configurations change. Do the following steps: 

cp ML_ABN ML_AB
cp CONTCAR POSCAR

and set the following INCAR tag 

ML_ISTART = 1

After running the calculation, we inspect the last instance of the errors in the ML_LOGFILE by typing: 

grep ERR ML_LOGFILE

The last few lines should have values close to: 

ERR                   675   5.10937061E-03   1.46895065E-01   2.50094941E+00
ERR                   861   5.10937061E-03   1.46895065E-01   2.50094941E+00
ERR                   924   5.10937061E-03   1.46895065E-01   2.50094941E+00
ERR                   942   5.01460989E-03   1.47816836E-01   2.47329693E+00
ERR                  1000   5.01460989E-03   1.47816836E-01   2.47329693E+00

We see that the accuracy has changed slightly. We also look at the ML_ABN file and the number of reference structures for training should increase compared to the run before 

 1.0 Version
**************************************************
     The number of configurations
--------------------------------------------------
         99

Also the number of local reference configurations (basis sets) increases compared to the previous calculation 

**************************************************
     The numbers of basis sets per atom type
--------------------------------------------------
       665


Ideally, one should continue learning until no structures need to be added to the training data and basis set anymore. Very often this can take up to 100ps depending on the material and conditions. In practice, the prediction of the Bayesian error exhibits numerical inaccuracies so that an ab initio calculation is conducted from time to time even if the force field is accurate enough. So measuring only the decreasing frequency of addition of new data is not sufficient to know when to finish. One should also look at the accuracy of the force on the training data and more importantly on the accuracy on some test data that is outside of the training sets.

Download

MLFF_Liquid_Si_tutorial.tgz

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