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I have generated a MLFF for Ge-Te alloys with the intention of using to make melt-quenched amorphous samples. This involves heating the sample above the melting point and then rapidly cooling it (at a rate, for example of 15K/ps)

For my first try, I used the following temperature ramp (and a NPT ensemble). I will attach a typical INCAR file. A refit run was completed at the end of the training. I have attached the LCONF lines from the ML_LOGFILE corresponding to each run. I noticed that upon quenching GeTe6 from melt, I would encounter isolated Te atoms in the melt-quench material which is are unlikely to exist. What suggestions would you give to improve the training? Should I use longer runs? The training was done on a small set of 54 atoms. The densities (NPT) seemed in reasonable agreement with experiment. The experimental melting point is roughly at 1000 K and I am interested in Te-rich compositions so 1500K is well above the melting point. I intended to randomize the initial structure using a temperature slightly higher than the melting point. (Is it too high for training?)

For reference, I have also loaded plots of the err_beef_ctifor values vs MD step in the attachment.

1. 400-600K 2,000 steps LCONF 2000 Ge 423 429 Te 411 421
2. 600-800K 5,000 steps LCONF 5000 Ge 657 704 Te 624 668
3. 1000-1200K 5,000 steps LCONF 5000 Ge 798 805 Te 733 741
4. 1200-1400K 5,000 steps LCONF 5000 Ge 1106 1113 Te 1003 1013
5. 1400-1600K 5,000 steps LCONF 5000 Ge 1692 1724 Te 1480 1506
6. 1500-1500K 10,000 steps LCONF 10000 Ge 4701 4753 Te 4440 4474
7. 1500-500K 10,000 steps LCONF 3687 Ge 8000 8027 Te 5672 5696

ALGO = Fast
BMIX = 1
ENMAX = 400
EDIFF = 1E-6
IBRION = 0
ISIF = 3
ISMEAR = 0
ISPIN = 1
ISYM = 0
KBLOCK = 100
LASPH = True
LCHARG = False
LMAXMIX = 4
LORBIT = 11
LPLANE = False
LREAL = False
LSCALU = False
LWAVE = True
NBLOCK = 1
NELM = 500
NELMIN = 4
NSW = 2000
POTIM = 2.0
PREC = Normal
SIGMA = 0.02

IVDW = 12

MDALGO = 3 ! Langevin thermostat
LANGEVIN_GAMMA = 10 10 ! friction
LANGEVIN_GAMMA_L = 10 ! lattice friction
PMASS = 10 ! lattice mass
TEBEG = 400 ! temperature
TEEND = 600

KPAR = 8
NCORE = 4

! machine learning
ML_MODE = train
ML_LMLFF = T
ML_ISTART = 0
ML_WTSIF = 2

I've moved your post here to "From users to users".

I've looked at your settings they look quite reasonable. I don't know how the training errors are because you didn't post your ML_LOGFILE of the last training step. Also I didn't see how many k-points you use but the KPAR tag suggest you use more than one.

Possible source of errors and what you could try:
-) Did you use a constraining of the lattice angles (see https://www.vasp.at/wiki/index.php/ICONST) when you train in the melt phase. Rodlike deformations are very common for liquids. This could have sneaked in in one of the runs and could have affected your calculations.
-) PBE-D3 (IVDW=11, 12) can sometimes lead to inconsistencies in the energies vs volume, because the coordination number can suddenly jump between volumes. So it's worth trying a different van der Waals like D2 (IVDW=10).
-) As you mentioned you can try longer runs (which means slower heating).
-) Try ALGO=Normal if you see any hints that the electronic convergence has problems (best look in the OSZICAR if at any step the maximum is reached - so possibly not converged).
-) At last try maybe increasing calculational parameters such as number of k-points, ENCUT, etc.

I notice two things:
-) You use ML_ISTART=0. That tag is deprecated, please use ML_MODE=train.
-) You use ML_WTSIF=2. If you for a special reason need to have the stress tensor more accurate and are not interested in the energies you can use that. Otherwise I think forces and energies are more important so I would not use that tag, since if you weight any of energy, forces or stress stronger it will make the others less accurate.

Dear Ferenc,
Thank you for your reply. To answer a few of your questions, I did use 8 k-points for the training. The ML_WTSIF=2 was specified in one of the tutorials on the vasp wiki so I used it believing it to be a hint. I will drop the use of this tag. I also was using IVDW 12, but I appreciate your comment about a sudden change in coordination could cause an energy jump due to the DFT-D3 term. I will try training again using the DFT-D2 correction as you suggest. I can also try ramping the temperature more slowly, but of course, this will take more time. I also did use the ICONST criteria to avoid cell deformation into a strange shape by constraining alpha,beta, gamm and two of the three axes lengths. I relaxed this constraint when the system temperature was below the melting point. The cell shape was reasonable throughout.

I was curious if you had any advice regarding the "ideal" or a reasonable number for LCONF. Also is there any sort of advice as to what the BEEF, ERR, and CTIFOR relationship should look like for a successfully trained system. I have attached the last plot of ERR, BEEF, and CTIFOR and the ML_LOGFILE for the last training.

One additional question is that as I aiming for a melt-quenched system, should I also train while reducing the temperature from the melt?

Thanks for any advice you can offer.

Yes you can also train by reducing the temperature but only after you already have a somewhat decent force field.