ML on system with H2O

Question on input files/tags, interpreting output, etc.

Please check whether the answer to your question is given in the VASP online manual or has been discussed in this forum previously!

Moderators: Global Moderator, Moderator

Message
Author
ferenc_karsai
Global Moderator
Global Moderator
Posts: 217
Joined: Mon Nov 04, 2019 12:44 pm

Re: ML on system with H2O

#16 Post by ferenc_karsai » Tue May 17, 2022 3:48 pm

Ok so I tested now and many things mainly from the DFT/MD settings were not ok.

There are some input parameters that need to be improved:
1) Do not run machine learning with ISIF=0. We do a multivariate fitting of energy, forces and stress. For ISIF=0 the stress is not calculated and the fitting won't work. This prevented the proper learning of the MLFF calculation. I will implement an automatic stop in the next release that prevents people using the machine learning with ISIF=0.
2) Also consider to run something like the D3 method for van der Waals (IVDW=11) and GGA=RP. The dDSC method (IVDW=4) that you set is not guaranteed to have the proper forces for machine learning. Also the D3 method is most reliable working with GGA=RP.
3) Increase the mass of hydrogen in the POTCAR file to 8 instead of 1. Then you can set POTIM=1.5. Otherwise the hydrogens will move too fast and uncontrolled.
4) Use ENCUT=700 to minimize the error for the stress tensor. This matters especially for single molecules, but in our case we have very few molecules so it could possibly matter.
5) Use LASPH = True.

I hope everything will run fine for you now. After this you can also try the graphene with H2O.

We are also working on a how to for proper training in machine learning.

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Re: ML on system with H2O

#17 Post by paulfons » Wed May 18, 2022 1:28 am

Dear Ferenc,
Thank you for your advice. I have already started a new training run. I am curious about the logic behind some of your recommendations. Most of the ideas, ENCUT=700 and ISIF=3, in retrospect are more or less obvious, but I am curious about the others. For instance, changing the mass of H in the POTCAR file to 8. Does this affect the accuracy of the calculated trajectories significantly? Also why is GGA=RP a better choice than PE (or some other functional) and in what way are the forces better for IVDW=11. I don't doubt you are correct, I just wish to have some insight into the choices so I can be proactive in choosing the right settings for future simulations. For instance in the past for relaxation calculations on a 2D chalcogenide system, I did a systematic comparison of lattice constants after relaxation after trying all of IVDW options (in vasp 5.4.3) and IVDW=4 gave better results. I suspect that the details of the vdW force algorithm are relevant here so a few words on the reasoning would be helpful for doing the right thing in the future.
It might also be a good idea to add the ISIF=3 to the Vasp wiki on machine learning (for instance why MAXMIX should not be set is explained very well).

Thanks a lot for your help!

ferenc_karsai
Global Moderator
Global Moderator
Posts: 217
Joined: Mon Nov 04, 2019 12:44 pm

Re: ML on system with H2O

#18 Post by ferenc_karsai » Wed May 18, 2022 2:06 pm

Ok next thing you have to be very careful:
If you run with ISIF=3 then you should constrain your lattice. In liquids I have almost always observed that your cell is going to be deformed monoclinically until it becomes like a thin rod. At that point your cell is irreversibly destroyed. In our case we have in principle a gas, but I fear the same would happen.
You can constrain the angles and lattice constant ratios by using the 3rd ICONST file from here:
https://www.vasp.at/wiki/index.php/ICONST

Alternatively in your case since you are not using a cubic cell you could use ISIF=2. In that case no volume changes are allowed.

We would also strongly recommend to use a Langevin thermostat, MDALGO=3. For ISIF=3 (NpT) ensemble that's anyway the only available thermostat.

Due to the small mass of hydrogen, very small time steps need to be used (<1fs), otherwise the simulations become unstable. An alternative to is to use a larger mass for hydrogen. This way still a larger timestep can be used. This is useful for on-the-fly learning where we want to collect snapshots on a larger trajectory as fast as possible. If you need strict trajectories (depending on the observables you need) switch back and use small time steps.

We advise you to use RP+D3 for water, since it is known from literature that RP is good for water and we have also used this combination in our thermodynamic integration paper with machine learning for water and it has worked fine.

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Problems with machine learning and H2O

#19 Post by paulfons » Wed Jun 15, 2022 7:52 am

I tried training a ML FF for water using Vasp 6.3.1. As suggested I have modified the mass of the Hydrogen atom to be 8 amu, ISIF=3, with cell constraints in ICONST. Since the H-O molecule has rather short bonds I used a value for ML_SION1 and ML_SION2 of 0.30. The temperature is set to ramp from 200 to 500 K over 5000 steps with an integration step of 1.5 fs. I have two questions. The ML run terminated due to insufficient storage space as ML_MB = 5000 so only 300 steps or so were executed. The temperature of the run exceeded 600K on the final 308 step. Continuing the run using the ML_ABN and ML_FFN files with a larger ML_MB setting of 10,000 resulted in the run hanging with the value of the temperature shooting up and eventually overflowing the temperature field. I have seen the same behavior in various similar scenarios, namely (for H2O) the temperature becomes unstable and shoots up until it overflows the field in OSZICAR and the run subsequently hangs. I am not sure of what to try next.

I am also curious to understand what is a reasonable goal for the size of the training, namely the number of configurations and training structures. I don't understand completely the details of the ML_LOGFILE, but I can grep LCONF in it. The last few lines of the grep are below for the 308 step run. I interpret this as there being slightly less than 5000 reference configurations and a total of 65 training structures (grep -c LCONF ML_LOGFILE gives the number 65). What is a desirable number of configuration and training structures. I have already started a second run with 300K as the temperature and a fixed (ICONST) cell to avoid the problem with diverging temperature. My plans are if this run complete with a couple of hundred training structures and 5000-8000 reference configurations, I will then introduce a cell with a graphene layer in it in addition to water molecules (again with a fixed cell) and add the additional interactions to the force field. Does this sound reasonable? Are there any other issues or diagnostics I should be concerned with in monitoring the progress of the ML FF training?

Code: Select all

LCONF                 275 H       4700      4739 O       2382      2403
LCONF                 285 H       4739      4784 O       2402      2426
LCONF                 292 H       4783      4832 O       2425      2451
LCONF                 300 H       4832      4881 O       2450      2476
LCONF                 307 H       4880      4925 O       2476      2500
LCONF                 314 H       4924      4971 O       2498      2522
LCONF                 319 H       4971      5012 O       2520      2544
The input files for the above results are attached below.
You do not have the required permissions to view the files attached to this post.

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Error: RATTLE_vel algorithm did not converge! err= NaN

#20 Post by paulfons » Thu Jun 16, 2022 2:51 am

I tried a second (new) run with both TEBEG and TEEND set to 300K, ISIF=3 to compute the stress tensor while using a Langevin thermostat with MDALGO=3. I have used ICONST to fix the cell dimensions and a time step of 1.5 fs after changing the mass of H to 8 amu in the POTCAR file. I have attached the INCAR file and other associated files for reference. During the run the temperature more or less monotonically increased from 300K to about 1600K upon which the run terminated due to the RATTLE_vel algorithm not converging. The settings for this run were from what I gather exactly what you (Ferenc) suggested. Can you offer some insight as to what should be changed?

I fixed the volume and shape of the simulation cell using the ICONST file below. I noticed upon a careful rereading of your earlier message that you suggested using the third example from the ICONST entry in the vasp wiki. Unless I am mistaken, this allowed for variable volume, but fixed the cell shape. I don't understand why this is a better option than fixing the cell volume (but I am doing another run using this option to check). Can you elaborate on the logic behind the variable cell volume/ fixed shape option?

Code: Select all

LR 1 0
LR 2 0
LR 3 0
LA 1 2 0
LA 1 3 0
LA 2 3 0
I know I am getting ahead of myself, but I am still optimistic that the training errors will be resolved and am would like to set up a plan for carrying out training on the H2O/graphene system.
The initial suggestion was to train with H2O and then after an initial training session, introduce a graphene sheet with the water molecules and train the system for interactions with C. If the problems can be solved, I am curious with how to decide training is sufficient. You stated earlier "Please expect around 1000-2000 training structures (ML_MCONF) and several thousand local reference configurations (ML_MB)."
From the (very helpful) comments in ML_LOGFILE it would seem that grepping the tag SPRSC reporting on sparsification offers these values in the form of "nstr_spar ... Number of reference structures after sparsification" and "nlrc_spar ... Number of local reference configurations after sparsification for this element". Is this correct. If so I assume I should be looking for 1000-2000 values of nstr_spar and several thousand local reference configurations via the quantity nlrc_spar . Is this correct. If all goes well and I proceed to training graphene and H2O together, what should the values of these quantities be for a reasonable training set?


# SPRSC nstr_prev ... Number of reference structures before sparsification
# SPRSC nstr_spar ... Number of reference structures after sparsification
# SPRSC el .......... Element symbol
# SPRSC nlrc_prev ... Number of local reference configurations before sparsification for this element
# SPRSC nlrc_spar ... Number of local reference configurations after sparsification for this element

Code: Select all

    240 T=  1581. E= -.43275244E+03 F= -.45359345E+03 E0= -.45357421E+03  EK= 0.20841E+02 SP= 0.00E+00 SK= 0.00E+00
 bond charge predicted
       N       E                     dE             d eps       ncg     rms          rms(c)
DAV:   1    -0.451863271227E+03    0.55226E+00   -0.29361E+02   376   0.394E+01    0.164E+01
RMM:   2    -0.459586864411E+03   -0.77236E+01   -0.36956E+01   371   0.697E+00    0.180E+01
RMM:   3    -0.458468649332E+03    0.11182E+01   -0.58793E+00   371   0.274E+00    0.213E+01
RMM:   4    -0.453537754950E+03    0.49309E+01   -0.36499E+00   389   0.163E+00    0.104E+01
RMM:   5    -0.453696974782E+03   -0.15922E+00   -0.63704E-01   313   0.714E-01    0.955E+00
RMM:   6    -0.452580704164E+03    0.11163E+01   -0.60897E-01   282   0.580E-01    0.138E+01
RMM:   7    -0.452022759660E+03    0.55794E+00   -0.18440E+00   337   0.971E-01    0.758E+00
RMM:   8    -0.451830711252E+03    0.19205E+00   -0.18537E-01   284   0.426E-01    0.870E+00
RMM:   9    -0.451763943923E+03    0.66767E-01   -0.21265E-02   247   0.218E-01    0.712E+00
RMM:  10    -0.452053172556E+03   -0.28923E+00   -0.72738E-02   289   0.334E-01    0.726E+00
RMM:  11    -0.452092287215E+03   -0.39115E-01   -0.19354E-01   316   0.464E-01    0.138E+01
RMM:  12    -0.451989254863E+03    0.10303E+00   -0.73097E-03   249   0.220E-01    0.136E+01
RMM:  13    -0.451720915878E+03    0.26834E+00   -0.78246E-03   225   0.103E-01    0.136E+01
RMM:  14    -0.451773841071E+03   -0.52925E-01   -0.30987E-04   147   0.408E-02    0.137E+01
RMM:  15    -0.451434606854E+03    0.33923E+00   -0.12759E-02   189   0.895E-02    0.473E+00
RMM:  16    -0.451576414548E+03   -0.14181E+00   -0.15747E-02   259   0.225E-01    0.684E+00
RMM:  17    -0.451634495264E+03   -0.58081E-01   -0.19473E-02   243   0.183E-01    0.680E+00
RMM:  18    -0.451533125181E+03    0.10137E+00   -0.16496E-02   217   0.130E-01    0.636E+00
RMM:  19    -0.451487383314E+03    0.45742E-01   -0.52534E-03   202   0.952E-02    0.522E+00
RMM:  20    -0.451445379399E+03    0.42004E-01   -0.35656E-03   205   0.941E-02    0.147E+00
RMM:  21    -0.451479791045E+03   -0.34412E-01   -0.28801E-03   177   0.605E-02    0.534E+00
RMM:  22    -0.451476075435E+03    0.37156E-02   -0.10349E-03   158   0.580E-02
    241 T=  1556. E= -.43213539E+03 F= -.45265027E+03 E0= -.45262914E+03  EK= 0.20515E+02 SP= 0.00E+00 SK= 0.00E+00
    242 T=  1590. E= -.44109121E+03 F= -.46205846E+03 E0= -.46205846E+03  EK= 0.20967E+02 SP= 0.00E+00 SK= 0.00E+00
    243 T=  1559. E= -.44075037E+03 F= -.46130621E+03 E0= -.46130621E+03  EK= 0.20556E+02 SP= 0.00E+00 SK= 0.00E+00
    244 T=  1546. E= -.44065648E+03 F= -.46103677E+03 E0= -.46103677E+03  EK= 0.20380E+02 SP= 0.00E+00 SK= 0.00E+00
    245 T=  1625. E= -.44100014E+03 F= -.46242998E+03 E0= -.46242998E+03  EK= 0.21430E+02 SP= 0.00E+00 SK= 0.00E+00
    246 T=  1594. E= -.44072307E+03 F= -.46173465E+03 E0= -.46173465E+03  EK= 0.21012E+02 SP= 0.00E+00 SK= 0.00E+00
 Error: RATTLE_vel algorithm did not converge! err=                     NaN
 -----------------------------------------------------------------------------
|                                                                             |
|     EEEEEEE  RRRRRR   RRRRRR   OOOOOOO  RRRRRR      ###     ###     ###     |
|     E        R     R  R     R  O     O  R     R     ###     ###     ###     |
|     E        R     R  R     R  O     O  R     R     ###     ###     ###     |
|     EEEEE    RRRRRR   RRRRRR   O     O  RRRRRR       #       #       #      |
|     E        R   R    R   R    O     O  R   R                               |
|     E        R    R   R    R   O     O  R    R      ###     ###     ###     |
|     EEEEEEE  R     R  R     R  OOOOOOO  R     R     ###     ###     ###     |
|                                                                             |
|     Error too large, I have to terminate this calculation!                  |
|                                                                             |
|       ---->  I REFUSE TO CONTINUE WITH THIS SICK JOB ... BYE!!! <----       |
|                                                                             |
 -----------------------------------------------------------------------------

Code: Select all

# LCONF ###############################################################
# LCONF This line shows the number of local configurations
# LCONF which were sampled from ab initio reference calculations.
# LCONF 
# LCONF nstep ...... MD time step or input structure counter
# LCONF el ......... Element symbol
# LCONF nlrc_old ... Previous number of local reference configurations for this element
# LCONF nlrc_new ... Current number of local reference configurations for this element
# LCONF ###############################################################
# LCONF             nstep el  nlrc_old  nlrc_new el  nlrc_old  nlrc_new
# LCONF                 2  3         4         5  6         7         8
# LCONF ###############################################################
LCONF                   1 H          0        66 O          0        33
LCONF                   2 H         65       131 O         32        65
LCONF                   3 H        131       197 O         65        98
LCONF                   4 H        197       263 O         98       131
LCONF                   5 H        259       325 O        129       162
LCONF                   6 H        317       383 O        158       191
LCONF                   7 H        374       440 O        186       219

    Multiple deleted lines ....

LCONF                 230 H       3911      3942 O       2010      2028
LCONF                 232 H       3941      3969 O       2027      2044
LCONF                 235 H       3969      3998 O       2044      2060
LCONF                 237 H       3998      4027 O       2060      2076
LCONF                 238 H       4026      4052 O       2076      2090
LCONF                 240 H       4052      4074 O       2090      2104
LCONF                 241 H       4074      4097 O       2104      2117
You do not have the required permissions to view the files attached to this post.

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

ML training and Error: RATTLE_vel algorithm did not converge! err= NaN

#21 Post by paulfons » Thu Jun 16, 2022 3:01 am

I am training a force field using ML for H2O in a box. This training is being done using Vasp 6.3.1. The job terminated with the error below.


I tried a second (new) run with both TEBEG and TEEND set to 300K, ISIF=3 to compute the stress tensor while using a Langevin thermostat with MDALGO=3. I have used ICONST to fix the cell dimensions and a time step of 1.5 fs after changing the mass of H to 8 amu in the POTCAR file. I have attached the INCAR file and other associated files for reference. During the run the temperature more or less monotonically increased from 300K to about 1600K upon which the run terminated due to the RATTLE_vel algorithm not converging. The settings for this run were from what I gather exactly what you (Ferenc) suggested. Can you offer some insight as to what should be changed?

Another question relates to diagnostics in ML_LOGFILE. If I grep the sparsification entries I can see that a typical SPRSC entry looks like below. For this line, am I correct in assuming there are 144 reference structures (training structures?) and for H there are 4096 local reference configurations while there are 2116 reference configurations for O. Is this correct?

Code: Select all

SPRSC                 241       114       114 H       4097      4096 O       2117      2116

I fixed the volume and shape of the simulation cell using the ICONST file below. I noticed upon a careful rereading of your earlier message that you suggested using the third example from the ICONST entry in the vasp wiki. Unless I am mistaken, this allowed for variable volume, but fixed the cell shape. I don't understand why this is a better option than fixing the cell volume (but I am doing another run using this option to check). Can you elaborate on the logic behind the variable cell volume/ fixed shape option?

Code: Select all

LR 1 0
LR 2 0
LR 3 0
LA 1 2 0
LA 1 3 0
LA 2 3 0
I know I am getting ahead of myself, but I am still optimistic that the training errors will be resolved and am would like to set up a plan for carrying out training on the H2O/graphene system.
The initial suggestion was to train with H2O and then after an initial training session, introduce a graphene sheet with the water molecules and train the system for interactions with C. If the problems can be solved, I am curious with how to decide training is sufficient. You stated earlier "Please expect around 1000-2000 training structures (ML_MCONF) and several thousand local reference configurations (ML_MB)."
From the (very helpful) comments in ML_LOGFILE it would seem that grepping the tag SPRSC reporting on sparsification offers these values in the form of "nstr_spar ... Number of reference structures after sparsification" and "nlrc_spar ... Number of local reference configurations after sparsification for this element". Is this correct. If so I assume I should be looking for 1000-2000 values of nstr_spar and several thousand local reference configurations via the quantity nlrc_spar . Is this correct. If all goes well and I proceed to training graphene and H2O together, what should the values of these quantities be for a reasonable training set?

Code: Select all

# SPRSC #######################################################################################################
# SPRSC This line shows the results of sparsification regarding the number
# SPRSC of reference structures and local reference configurations.
# SPRSC 
# SPRSC nstep ....... MD time step or input structure counter
# SPRSC nstr_prev ... Number of reference structures before sparsification
# SPRSC nstr_spar ... Number of reference structures after sparsification
# SPRSC el .......... Element symbol
# SPRSC nlrc_prev ... Number of local reference configurations before sparsification for this element
# SPRSC nlrc_spar ... Number of local reference configurations after sparsification for this element
# SPRSC #######################################################################################################
# SPRSC             nstep nstr_prev nstr_spar el nlrc_prev nlrc_spar nstr_prev nstr_spar el nlrc_prev nlrc_spar
# SPRSC                 2  3         4         5         6         7  8         9        10        11        12
# SPRSC #######################################################################################################
SPRSC                   1         1         1 H         66        65 O         33        32
SPRSC                   2         2         2 H        131       131 O         65        65
SPRSC                   3         3         3 H        197       197 O         98        98
SPRSC                   4         4         4 H        263       259 O        131       129

SKIPPED LINES

SPRSC                 230       108       108 H       3942      3941 O       2028      2027
SPRSC                 232       109       109 H       3969      3969 O       2044      2044
SPRSC                 235       110       110 H       3998      3998 O       2060      2060
SPRSC                 237       111       111 H       4027      4026 O       2076      2076
SPRSC                 238       112       112 H       4052      4052 O       2090      2090
SPRSC                 240       113       113 H       4074      4074 O       2104      2104
You do not have the required permissions to view the files attached to this post.

martin.schlipf
Global Moderator
Global Moderator
Posts: 276
Joined: Fri Nov 08, 2019 7:18 am

Re: ML on system with H2O

#22 Post by martin.schlipf » Fri Jun 17, 2022 7:26 am

I merged the topics as the discussion seem to all concern the same system.

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Re: ML on system with H2O

#23 Post by paulfons » Fri Jun 17, 2022 7:34 am

Thank you for merging the topics. I initially tried to add to my original post, but when submitting the post, the forum responded with a file permission error. Thus I made a new post. I am curious about the answer to the questions. I have tried yet one more time to train the FF for H2O with the temperature fixed at 300K, but over the course of 400 steps, the temperature has creeped up to about 2000K. As ISIF=3, I am using a Langevin themostat and a time step of 1.5fs. Is my time step too big even though I increased the pass of H to 8 amu to avoid problems with too big time steps? Any advice is welcome!

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Re: ML on system with H2O

#24 Post by paulfons » Fri Jun 17, 2022 11:50 am

I have attempted a new ML learning session with H2O. In this run I again used the Langevin, but did not attempt to ramp the temperature, but left it set at 300K. I used a time step of 1.5 fs (POTIM) and set the mass of H to 8 amu to allow sampling of a larger subset of phase-space without running into integration problems. The problem here again is that the temperature drifted up from 300K to about 5500K before the run crashed with the following error. I am at a loss as to how to train the ML FF.
Although the INCAR file is attached, I will mention that I used the following machine learning parameters

Code: Select all

#ML_SION1 = 0.30
#ML_SION2 = 0.30
ML_MB = 10000
#ML_IALGO_LINREG=1
#ML_MRB1=12 
e.g. As the bond length in H2O is about an Angstrom, I change ML_ION1 and ML_ION2 to 0.3 Angstroms and increased the number of basis functions to 12. Any suggestions as to what to try next are most welcome.

Code: Select all

MM:  37    -0.420936430149E+03   -0.39800E-02   -0.19248E-03   201   0.131E-01
Abort(537501702) on node 31 (rank 31 in comm 0): Fatal error in PMPI_Irecv: Invalid rank, error stack:
PMPI_Irecv(167): MPI_Irecv(buf=0x7ffe991ca270, count=1, MPI_DOUBLE, src=-25, tag=15, comm=0xc4000010, request=0x7ffe991c9da0) failed
PMPI_Irecv(95).: Invalid rank has value -25 but must be nonnegative and less than 32
You do not have the required permissions to view the files attached to this post.

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Re: ML on system with H2O

#25 Post by paulfons » Tue Jun 21, 2022 2:08 am

A small update. Even though I had modified the atomic weight of H to 8 amu, I was still suspicious of the time step of 1.5 fs being too large. I changed the time step to 0.5 (still with H=8 amu) and restarted the run. The run using using the Langevin thermostat with fixed cell size and shape with a temperature ramp from 300 to 500 K. The temperature has crept upwards similar to before, but seems to have stabilized at slightly less than 800K for the last few hundred steps. As the electronic structure is converging, I am still hoping this is providing good training data for the force field.

I now have a few hundred training steps with several thousand configurations for both H and O. What would be a good place to stop at?

Code: Select all

SPRSC                1073       152       152 H       4493      4475 O       1993      1969
SPRSC                1110       156       156 H       4590      4576 O       2039      2020
SPRSC                1128       158       158 H       4638      4627 O       2054      2040
SPRSC                1146       160       160 H       4694      4685 O       2075      2063
SPRSC                1169       163       163 H       4802      4791 O       2122      2105
SPRSC                1219       168       168 H       4976      4962 O       2196      2175
SPRSC                1232       170       170 H       5016      5007 O       2206      2189
SPRSC                1255       173       173 H       5094      5084 O       2237      2220
SPRSC                1305       178       178 H       5231      5218 O       2304      2282
SPRSC                1355       183       183 H       5356      5343 O       2345      2323
SPRSC                1374       185       185 H       5390      5380 O       2350      2335
SPRSC                1424       190       190 H       5522      5510 O       2407      2390
SPRSC                1474       195       195 H       5640      5626 O       2466      2446
SPRSC                1507       199       199 H       5723      5713 O       2501      2483
SPRSC                1557       204       204 H       5844      5832 O       2545      2527
SPRSC                1607       209       209 H       5937      5927 O       2578      2562
SPRSC                1644       213       213 H       6041      6027 O       2618      2602
SPRSC                1683       217       217 H       6170      6156 O       2671      2652

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Langevin thermostat

#26 Post by paulfons » Wed Jun 22, 2022 5:54 am

I think I may have answered my own question about the cause of the temperature instability, namely the parameters for the Langevin thermostat required by MDALGO=3 using the method of Parrinello and Rahman.

The parameters are

1. LANGEVIN_GAMMA : the friction coefficients γ (in ps-1) for each atom type
2. LANGEVIN_GAMMA_L (the friction coefficient (in ps-1) for lattice degrees-of-freedom)
3. PMASS (mass for the lattice degrees-of-freedom)

Can anyone offer insight on what values I should use for a system containing H2O atoms in a big box?

ferenc_karsai
Global Moderator
Global Moderator
Posts: 217
Joined: Mon Nov 04, 2019 12:44 pm

Re: ML on system with H2O

#27 Post by ferenc_karsai » Wed Jun 22, 2022 11:15 am

So in none of your calculations I can see that you have changed the mass of hydrogen.

I just grepped in your latest calculation for POMASS and I get the following:
POMASS = 1.00 16.00

So H has still a mass of 1.
You need to modify your POMASS for H in the POTCAR file or set the values in the INCAR file:
https://www.vasp.at/wiki/index.php/POMASS

Also you have set a way too large energy cut-off for the energetic convergence: EDIFF = 0.00428

A good practice is, try to set as few as possible tags and order the INCAR file, the defaults usually work well. Already the ISIF tag and the EDIFF were set really wrongly.

I have made an INCAR for you now, please try that:
ENCUT = 700
GGA = RP
ALGO = Fast
IBRION = 0
ISIF = 3
MDALGO=3
LANGEVIN_GAMMA = 10.0 10.0
LANGEVIN_GAMMA_L = 3.0
PMASS = 100
ISMEAR = 0
ISYM = 0
LASPH = True
LCHARG = False
LREAL = Auto
ML_ISTART = 0
ML_LMLFF = True
ML_MB = 10000
NCORE = 2
NSW = 10000
POTIM = 1.5
PREC = Normal
TEBEG = 300
TEEND = 500
IVDW = 11
POMASS = 8.0 16.0

ferenc_karsai
Global Moderator
Global Moderator
Posts: 217
Joined: Mon Nov 04, 2019 12:44 pm

Re: ML on system with H2O

#28 Post by ferenc_karsai » Wed Jun 22, 2022 11:29 am

Concerning the Langevin thermostat (as for all thermostats), the results should be independent of it's parameters.

Of course if the damping is to large your system will not move enough or in worst case won't be ergodic.
If the value is too large you can get huge uncontrollable fluctuations and deformations.

What I have usually experienced is that the calculation is not very sensitive to the values of LANGEVIN_GAMMA, PMASS and LANGEVIN_GAMMA_L so I just carry the values over from one calculation to the other.

Of course to be really safe you need to check the effect of the thermostat/barostat parameters for each new system by varying each parameter and comparing the change of the desired quantity.

User avatar
paulfons
Jr. Member
Jr. Member
Posts: 53
Joined: Sun Nov 04, 2012 2:40 am
License Nr.: 5-1405
Location: Yokohama, Japan
Contact:

Re: ML on system with H2O

#29 Post by paulfons » Thu Jun 23, 2022 9:25 am

Dear Ferenc,
I seemed to have solved the problem with the LANGEVIN Gamma and the system has remained within a reasonable proximity of the 300K setting I used.
I am curious to know what constitutes a sufficient number of reference configurations. Currently I have ML_MB = 10000 and the run terminated due to insufficient memory for the number of configurations stored.
I can see that when it terminated there were 9683 configurations for O and 4092 configurations for H stored along with a total of 192 samples. Is this a reasonable amount of configurations? Should I increase ML_MB more and continue for H2O before adding carbon? Thanks for your help (and patience!)

Code: Select all

SPRSC                1152       162       162 H       8170      8139 O       3566      3505
SPRSC                1202       167       167 H       8460      8430 O       3670      3603
SPRSC                1252       172       172 H       8751      8723 O       3766      3694
SPRSC                1302       177       177 H       9044      9015 O       3859      3778
SPRSC                1352       182       182 H       9336      9307 O       3943      3858
SPRSC                1402       187       187 H       9621      9592 O       4023      3930
SPRSC                1452       192       192 H       9896      9863 O       4092      4000
(

ferenc_karsai
Global Moderator
Global Moderator
Posts: 217
Joined: Mon Nov 04, 2019 12:44 pm

Re: ML on system with H2O

#30 Post by ferenc_karsai » Thu Jun 23, 2022 11:32 am

Please post the input and output files of your last calculation (POSCAR, KPOINTS, POTCAR, INCAR, ML_LOGFILE, OSZICAR)

Post Reply