Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets. Our RL strategy utilizes decision trees coupled with a hierarchical reward strategy to accelerate convergence in high-dimensional continuous search spaces. Unlike the Stillinger-Weber approach, which demands separate formalisms for buckled and puckered forms, the ML-Tersoff model concurrently captures multiple properties of the two polymorphs by effectively representing the local environment, thereby avoiding the need for different atomic types. We apply the ML model to understand the mechanical and thermal properties of the arsenene polymorphs and nanostructures. We observe an inverse relationship between the critical strain and temperature in arsenene. Thermal conductivity calculations in nanosheets show good agreement with ab initio data, reflecting a decrease in thermal conductivity attributable to increased anharmonic effects at higher temperatures. We also apply the model to predict the thermal behavior of arsenene nanotubes.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1021/acs.jpca.4c01040 | DOI Listing |
J Phys Chem A
July 2024
Department of Mechanical and Industrial Engineering, University of Illinois, Chicago, Illinois 60607, United States.
Arsenene, a less-explored two-dimensional material, holds the potential for applications in wearable electronics, memory devices, and quantum systems. This study introduces a bond-order potential model with Tersoff formalism, the ML-Tersoff, which leverages multireward hierarchical reinforcement learning (RL), trained on an ab initio data set. This data set covers a spectrum of properties for arsenene polymorphs, enhancing our understanding of its mechanical and thermal behaviors without the complexities of traditional models requiring multiple parameter sets.
View Article and Find Full Text PDFNanotechnology
March 2021
Institute of High Performance Computing, A*STAR, Singapore 138632, Singapore.
In this work, we predict a new polymorph of 2D monolayer arsenic. This structure, named-As, consists of a centrosymmetric monolayer, which is thermodynamically and kinetically stable. Distinctly different from the previously predicted monolayer arsenic with an indirect bandgap, the new allotrope exhibits a direct bandgap characteristic.
View Article and Find Full Text PDFPhys Chem Chem Phys
February 2021
School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510006, China.
Single-layer δ-As and γ-P have unique atomic arrangement, which belong to the Pmc21 and Pbcm space groups, respectively. Because of the coupling hinge structure, the physical properties of the two materials have obvious anisotropy. In this paper, we report the mechanical properties of the single-layer δ-As and γ-P.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!