Structure and mechanical properties of monolayer amorphous carbon and boron nitride

Abstract

Amorphous materials exhibit various characteristics that are not featured by crystals and can sometimes be tuned by their degree of disorder (DOD). Here, we report results on the mechanical properties of monolayer amorphous carbon (MAC) and monolayer amorphous boron nitride (maBN) with different DOD. The pertinent structures are obtained by kinetic-Monte-Carlo (kMC) simulations using machine-learning potentials (MLP) with density-functional-theory (DFT)-level accuracy. An intuitive order parameter, namely the areal fraction Fx occupied by crystallites within the continuous random network, is proposed to describe the DOD. We find that Fx captures the essence of the DOD: Samples with the same Fx but different sizes and distributions of crystallites have virtually identical radial distributions functions as well as bond-length and bond-angle distributions. Furthermore, by simulating the fracture process with molecular dynamics, we found that the mechanical responses of MAC and maBN before fracture are solely determined by Fx and are insensitive to the sizes and specific arrangements of the crystallites. The behavior of cracks in the two materials is analyzed and found to mainly propagate in meandering paths in the CRN region and to be influenced by crystallites in distinct ways that toughen the material. The present results reveal the relation between structure and mechanical properties in amorphous monolayers and may provide a universal toughening strategy for 2D materials.

References in corpus (14)

• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• Synthesis and properties of free-standing monolayer amorphous carbon
• Dynamic Fracture in Single Crystal Silicon
• Toughness and Strength of Nanocrystalline Graphene
• Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
• Screened empirical bond-order potentials for Si-C
• Machine learning driven simulated deposition of carbon films: from low-density to diamondlike amorphous carbon
• On the Mechanical and Thermal Stability of Free-standing Monolayer Amorphous Carbon
• The structure of amorphous two-dimensional materials: Elemental monolayer amorphous carbon versus binary monolayer amorphous boron nitride
• Exploring the configurational space of amorphous graphene with machine-learned atomic energies
• Thermal transport of amorphous carbon and boron-nitride monolayers
• Quantitative prediction of the fracture toughness of amorphous carbon from atomic-scale simulations