Lattice thermal conductivity and mechanical properties of the single-layer penta-NiN₂ explored by a deep-learning interatomic potential

Penta-NiN2, a novel pentagonal 2D sheet with potential nanoelectronic applications, is investigated in terms of its lattice thermal conductivity, stability, and mechanical behavior. A deep learning interatomic potential (DLP) is firstly generated from ab initio molecular dynamics (AIMD) data and then utilized for classical molecular dynamics simulations. The DLP's accuracy is verified, showing strong agreement with AIMD results. The dependence of thermal conductivity on size, temperature, and tensile strain, reveals important insights into the material's thermal properties. Additionally, the mechanical response of penta-NiN2 under uniaxial loading is examined, yielding a Young's modulus of approximately 368 GPa. The influence of vacancy defects on mechanical properties is analyzed, demonstrating a significant reduction in modulus, fracture stress, and ultimate strength. This study also investigates the influence of strain on phonon dispersion relations and phonon group velocity in penta-NiN2, shedding light on how alterations in the atomic lattice affect the phonon dynamics and, consequently, impact the thermal conductivity. This investigation showcases the ability of deep learning-based interatomic potentials in studying the properties of 2D penta-NiN2.