Reasonable NPT constraints in ML
Posted: Thu Jul 11, 2024 8:06 am
I would like to melt quench some poor glass formers (phase change materials) in the computer and look at the electronic structure (among other qualities). I have successfully done this using NVT ensembles with MD and have found some interesting results. In a test, I melt-quenched MoS2 and then recrystallized it successfully using all ab-initio calculations. The amount of time and size limitations of these calculations are strong constraints. To get around this, I plan to use the ML feature of Vasp to "learn" the potential to allow me to vary both the size of the cluster as well as to carry out further investigations of the effects of quench rate. To train the system, I have been carrying out training using 10,000 steps for various 400K, 1000K, 1500K temperatures. For the training, I used a hexagonal cell with 64 atoms. I note that when I exceeded the melting point for higher temperatures, the cell shape became highly distorted and I realized, it was necessary to constrain the system. Initially I was using ICONST just to monitor for the system, but for temperatures approaching the melting point, I fixed all of the angles of the cell and two of the bond-lengths and only left the "c" direction as free to vary. Is this a reasonable way forward or should I use fewer constraints, e.g. for example just fixing the a and b axes while letting the c axis vary along with the cell angles. Do you have a recommendation for this (or other keywords in my INCAR file). The results (up to melting seem very reasonable) so I am looking forward to trying the same idea on other systems.
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