Machine Learning Potential for Hexagonal Boron Nitride Applied to Thermally and Mechanically Induced Rippling

TL;DR

This paper presents a machine learning interatomic potential for hexagonal boron nitride (hBN) that accurately reproduces DFT results and enables large-scale simulations of rippling phenomena, outperforming traditional methods in efficiency.

Contribution

The authors develop a Gaussian approximation potential for hBN trained on DFT data, capable of modeling bulk, multilayer, and nanotube structures with high fidelity and efficiency.

Findings

The potential accurately reproduces DFT formation energies and phonon spectra.

hBN and graphene exhibit similar rippling scaling behavior with an exponent of approximately 0.85.

hBN shows larger out-of-plane deviations due to lower bending resistance.

Abstract

We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density functional theory (DFT) simulations and is capable of treating bulk and multilayer hBN as well as nanotubes of arbitrary chirality. The developed force field faithfully reproduces the potential energy surface predicted by DFT while improving the efficiency by several orders of magnitude. We test our potential by comparing formation energies, geometrical properties, phonon dispersion spectra and mechanical properties with respect to benchmark DFT calculations and experiments. In addition, we use our model and a recently developed graphene-GAP to analyse and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behaviour with an exponent of $\eta \approx 0.85$ for the height fluctuations agreeing well with the theory of flexible membranes. Based on its lower resistance to bending, however, hBN experiences slightly larger out-of-plane deviations both at zero and finite applied external strain. Upon compression a phase transition from incoherent ripple motion to soliton-ripples is observed for both materials. Our potential is freely available online at [http://www.libatoms.org].