Optimal training schedule for recrystallization
I am investigating the recrystallization dynamics of the 2D material MoS2 from the amorphous phase. I have carried out a first-principles simulation and have observed the details of the crystallization process. I would like to scale up the process using machine learning.
The melting point of the system is quite high (2000K). In my first-principles simulation, after equilibrating at 1800K, I then quenched the system to room temperature. I then heated the system to 1800K and after about a couple of 100 ps, the system crystallized.
I would like to carry out the same recrystallization process with more atoms.. As the computational costs are quite high, I would like to do this using machine learning. I am curious how to optimize the machine learning process. I assume I should start out with the system in the crystalline state and then gradually raise the temperature to just below the melting point with the usual ICONST and NPT boundary conditions. What is a good target for the number of configurations for each atom type? If there is there any additional advice what can be offered, it would be most gratefully received.