Lattice thermal conductivity and elastic modulus of XN4 (X=Be, Mg and Pt) 2D materials using machine learning interatomic potentials

TL;DR

This study uses machine learning interatomic potentials to investigate the anisotropic mechanical and thermal properties of newly synthesized 2D materials BeN4, MgN4, and PtN4, revealing their elastic and thermal anisotropy.

Contribution

It introduces a machine learning-based interatomic potential for simulating and analyzing the mechanical and thermal properties of nitrogen-rich 2D materials.

Findings

Elastic modulus and thermal conductivity are higher in the armchair direction.

Elastic anisotropy remains constant with temperature for BeN4 and MgN4.

For PtN4, elastic anisotropy decreases as temperature increases.

Abstract

The newly synthesized BeN4 monolayer has introduced a novel group of 2D materials called nitrogen-rich 2D materials. In the present study, the anisotropic mechanical and thermal properties of three members of this group, BeN4, MgN4, and PtN4, are investigated. To this end, a machine learning-based interatomic potential (MLIP) is developed on the basis of the moment tensor potential (MTP) method and utilized in classical molecular dynamics (MD) simulation. Mechanical properties are calculated by extracting the stress-strain curve and thermal properties by non-equilibrium molecular dynamics (NEMD) method. Acquired results show the anisotropic elastic modulus and lattice thermal conductivity of these materials. Generally, elastic modulus and thermal conductivity in the armchair direction are higher than in the zigzag direction. Also, the elastic anisotropy is almost constant at every temperature for BeN4 and MgN4, while for PtN4, this parameter is decreased by increasing the temperature. The findings of this research are not only evidence of the application of machine learning in MD simulations, but also provide information on the basic anisotropic mechanical and thermal properties of these newly discovered 2D nanomaterials.