  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.
