Modelling defective 2D graphene and hBN

Hello everyone

I am trying to simulate a sheet of graphene with randomly placed mono- and divacancy defects at a concentration of 0.25%, i.e., 10 deleted atoms in a 10x10nm sheet of 4032 atoms. I use the delete_atoms command with the “random” argument however the thermal conductivity value increases by 10x. The expected value is a 50% reduction. I have used visualizers and looked at the data file generated and everything looks to be in order in terms of defect placement and number of deleted atoms. I have also tested the code without the delete_atoms command and found the results to be consistent with expectations. I am using the Green-Kubo method.

I am using the 23Jun2022 version of LAMMPS and would hope to keep it for consistency with my previous publications.

My question now is whether the delete_atoms command is appropriate for this situation or if there is a more suitable command. I have included the log and input files for both defective and pristine graphene.

Thanks in advance
Darren
SLG-GKpristine.in (4.9 KB)
SLG-GKdefective.in (4.9 KB)
logSLG100x100_pristine_1eql.lammps (22.1 KB)
logSLG100x100_defective_1eql.lammps (22.0 KB)

Hi Darren,

Did you try deleting the atoms before the equilibration? You do the opposite and I think that should be corrected.

Sincerely,
Erhan

Thank you for the reply.

Yes, I did that and it solved the issue.