Apr 22, 2024
9:30am - 9:45am
Room 320, Level 3, Summit
Yuyang Zhang1,Xi Zhang1,Yupeng Wang2,Shixuan Du1,Sokrates Pantelides3
Chinese Academy of Sciences1,Central South University2,Vanderbilt University3
Yuyang Zhang1,Xi Zhang1,Yupeng Wang2,Shixuan Du1,Sokrates Pantelides3
Chinese Academy of Sciences1,Central South University2,Vanderbilt University3
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 structures, mechanical and thermal properties of monolayer amorphous carbon (MAC) and monolayer amorphous boron nitride (maBN). The pertinent structures are obtained by kinetic-Monte-Carlo (kMC) simulations separately using empirical potential [1,2] and machine learning potentials (MLP) [3]. We find that despite conducting extensive validation on the reliability of empirical potentials for kMC simulations, and obtaining results consistent with DFT, kMC simulations using empirical potentials and more accurate MLP still yield significantly different results. Z-CRN containing crystallite instead of compositionally disordered “pseudocrystallite” is the favored structure of both elemental MAC and binary maBN. An intuitive order parameter, namely the areal fraction Fx occupied by crystallites within the Z-CRN, is proposed to describe the DOD. The mechanical and thermal properties of MAC and maBN were calculate via molecular dynamics simulation. The mechanical properties of MAC and maBN were found to be solely determined by Fx and are insensitive to the sizes and arrangements of crystallite. On the other hand, about two orders of magnitudes reduction in thermal conductivity was found in monolayer carbon after amorphization. The present results demonstrate the superiority of MLP over empirical potentials in the study of amorphous materials and reveal the relation between structures and properties in monolayer amorphous materials.<br/>References:<br/>[1] Y.-T. Zhang, et al., Nano Letters, 22, 8018 (2022).<br/>[2] Y.-T. Zhang, et al., Applied Physics Letters 120, 222201 (2022).<br/>[3] X. Zhang, et al., https://arxiv.org/abs/2309.15352