NNP-nonstoichiometric-chromium-sulfides

This repository contains the data and codes utilized in this paper "Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations". For further details, please refer to the published paper here.

SA.py (Simulated Annealing)

Overview

simulated_annealing/SA.py is a Python script for optimizing vacancy defects in a lattice-based supercell using simulated annealing. It utilizes LAMMPS-MPI with n2p2 NNP for energy calculations and ASE for structural manipulations.

Features

• Initializes a bulk supercell with random vacancy defects in a certain sublattice.
• Employs cyclic simulated annealing with Monte Carlo atom/vacancy swaps and cell perturbations to identify the ground-state and meta-stable defect distributions.
• Outputs optimized structures and energy metadata for each annealing cycle.

MC.py (Vacancy Diffusion Monte Carlo Simulation)

Overview

MC_vacancy_diffusion/MC.py performs nearest-neighbor vacancy diffusion Monte Carlo simulations on laterally-strained lattice-based supercells of finite-thickness slabs. It utilizes LAMMPS-MPI with n2p2 NNP for energy minimizations.

Features

• Initializes a finite-thickness slab supercell with random vacancy defects in a certain sublattice.
• Employs nearest-neighbor vacancy diffusion between vacancies and their nearby atoms, and tracks structural evolution.
• Outputs structure evolution trajectories, inter-layer/intra-layer vacancy migration statistics, energy/force profiles across the simulated slab sample, etc.

Dependencies

• Python 3.x
• MPI (mpi4py)
• LAMMPS
• ASE (Atomic Simulation Environment)
• NumPy, Pandas, SciPy
• Pymatgen

Configuration

Modify the input parameters in the scripts to align with your system and simulation settings.

Modeling Strain-Induced van der Waals Gaps

The following visual illustrates the strain-induced vdW gaps (non-vdW to vdW phase transition) in CrS2 slabs.