Graphene Neural Network Potential

This repo (graphne-nnp) presents the interatomic potential development for a graphene monolayer using high-dimensional neural network methodology as implemented in the RuNNer code and the N2N2 C++ library interfaced with LAMMPS package.

Before using it:

Make sure that the RuNNer code and LAMMPS with pair_style nnp are built on your machine. For more details, please refer to the documentations.

How does it work?

1. The md.airebo.in script reads the initial graphene structure grn.lmp from lmp directory, performing a molecular dynamics (MD) simulation in order to generate the initial dataset, and save it into airebo.data.

lmp_serial < md.airebo.in > lmp/md.airebo.out

2. The lammps_to_runner.py Python script converts the initial dataset airebo.data to the RuNNer input data format input.data.

python lammps_to_runner.py lmp/airebo.data nnp/input.data

3. Using RuNNer in mode 1 and 2 in order to generate symmetric functions and train the neural network potential (NNP), respectively.

cd nnp
sh runscript.sh > runscript.out
cd ..

4. The md.nnp.in performs MD simulation using developed NNP in nnp directory and predicts trajectory of atoms for several next time steps (i.e. 100) and save the configurations into nnp.data.

rm -f lmp/nnp.data
lmp_serial < md.nnp.in > lmp/md.nnp.out

5. The rerun.airebo.in reads nnp.data structure file and performs single point calculation for each snapshot (configuration) obtained from previous step including energy, forces, charges, etc.

6. The otained data in previous step is appended to the airebo.data and repeating from the step 2.

lmp_serial < rerun.airebo.in > lmp/rerun.airebo.out

This procedure constantly increases the size of dataset in order to effectively find holes in potential energy surface. It is eventually has to be stopped when a desired accuracy achieved. At this point, pred.md.nnp.in can be used to perform the desired large-scale MD simulation. For sake of comparison, pred.md.airebo.in script with the AIREBO potential is also available.

All the steps can automatically apply by running train_nnp.sh.

Evaluating NNP:

Primary comparison between the trained NNP and the AIREBO potentials for a supercell of size of 2x2x1 (448 atoms) are found as follows:

• Radial distribution functions

• Energy per atom as function of temperature