Machine learning for a melt-quench process
I am looking at melt-quenched chalcogenide amorphous structures and I am hoping to use machine learning to sample more structures (quench rate dependence) than I would otherwise be able to achieve all first principle calculations. In the melt-quench process (think phase-change), the allow is quenched from the liquid to the amorphous state. Thus I would like to use the machine learned potential to investigate two degrees of freedom, namely cooling rate and the effect of system size (more atoms).
The system I am studying is Ge-Hf-Te. For the calculations, I started with the binary Ge-Te in a 1:6 ration. As there does not crystallize in a single phase, I created the cell by using the structure of GeTe substituting Te for Ge to ensure the correct composition. I then adjusted the density to match experiment.
I carried out the machine learning process in several stages using 64 atoms in total with a total of 8 kpoints to improve accuracy. I am using Vasp 6.4.3.
600K-800K in 10,000 steps
refit
800K-1000K in 10,000 steps
1000K-1400K in 10,000 steps
1400K-1800K in 10,000 steps
This resulted in the following number of reference sites
# LCONF ###############################################################
# LCONF nstep el nlrc_old nlrc_new el nlrc_old nlrc_new
# LCONF 2 3 4 5 6 7 8
# LCONF ###############################################################
LCONF 567 Ge 3332 3361 Te 5340 5383
I then substituted about 10% Hf onto the Te sites and continued the machine learning process.
400K in 10,000 steps
refit
400-600K in 10,000 steps
refit
1000K-1200K in 10,000 steps
This resulted in the following number of reference sites
# LCONF ###############################################################
# LCONF nstep el nlrc_old nlrc_new el nlrc_old nlrc_new
# LCONF 2 3 4 5 6 7 8
# LCONF ###############################################################
LCONF 3 Ge 4406 4415 Te 8329 8377 Hf 1716 1722
I am curious to get some feedback on how reasonable this process is and what I should change, if anything.