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

Title: 基于机器学习原子间势的XN4（X=Be、Mg和Pt）二维材料的晶格热导率和弹性模量

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. Show Chinese abstract

Abstract: 新合成的BeN4单层引入了一类称为富氮二维材料的新二维材料。 在本研究中，对这一类别的三个成员BeN4、MgN4和PtN4的各向异性力学和热学性能进行了研究。 为此，基于张量矩势（MTP）方法开发了一种基于机器学习的原子间势（MLIP），并用于经典分子动力学（MD）模拟。 力学性能通过提取应力-应变曲线计算，热学性能通过非平衡分子动力学（NEMD）方法计算。 获得的结果显示了这些材料的各向异性弹性模量和晶格热导率。 一般来说，扶手椅方向的弹性模量和热导率高于锯齿方向。 此外，BeN4和MgN4的弹性各向异性在所有温度下几乎保持不变，而PtN4的该参数随着温度的升高而降低。 这项研究的发现不仅证明了机器学习在MD模拟中的应用，还提供了这些新发现的二维纳米材料的基本各向异性力学和热学性能的信息。