Authors: Kun Zhou | Bo Liu | Yongqing Cai | Sergey V. Dmitriev | Shaofan Li
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2022-03-01)
Field: Materials Science — Materials Chemistry

Modelling of Low‐dimensional Functional Nanomaterials Atomic activities and processes largely govern the macroscopic properties and performance of materials. Traditional experiments have difficulties in uncovering such information with atomic-scale resolution and ultrashort timescale. For over a century, most of the data in hard science are generated by experiments and models are developed through fitting the experimental data with phenomenological analytic forms. However, in nanomaterials, the lack of experimental data and the emergence of quantum behaviors make the theoretical modelling quite limited via traditional physical intuition. The past decades have witnessed the rapid progress on the computational power and advanced theoretical modeling algorithms. In particular, the applications of atomic-scale simulations, including molecular dynamics (MD) simulation and first-principles calculation, have gained ever-increasing popularity among the scientific and industrial communities of materials science [1]. This is indicated by the number of papers published for studies based on MD simulations and density functional theory (DFT) calculations, as shown in the Figure 1a. A large number of these studies have been devoted to studying surfaces, interfaces, and bulk systems to understand various problems including mechanical deformation, evolution of defects, electronic structure, and materials growth [2]. In particular, the combination of atomic-scale simulation and machine learning (ML) has recently gained extensive attention (Figure 1b) due to its great potential for tackling complex systems, and are believed to change the way of material design and discovery [3]. It is a great pleasure for us to contribute to this Special Issue on modelling and simulation of nanomaterials. This issue serves as a good compilation of the potential tasks and problems in nanomaterials that can be approached by modelling and simulations. Below are the Highlights in this issue. By using large-scale atomistic molecular simulations, Xu et al. (article number 202100298) investigated the tribological properties and friction behaviors of the polytetrafluoroethylene (PTFE) composites reinforced by the one-dimensional (1D) and two-dimensional (2D) carbon polymorphs: carbon nanotubes (CNTs) and graphene (Gr). The hybrids with the CNT and Gr fillers have a high wear resistance under a high pressure compared with the pure PTFE. In addition, both fillers enhance the internal deformation resistance of the bulk composite. The addition of CNT and Gr fillers can accommodate the relative motions at the tribological interfaces, which allows a thin tribo-film compared with that of pure PTFE. Atomic scale simulation was also adopted to clarify the impact process between a particle and a substrate. Lu et al. (article number 202100412) studied the erosion of diamond-like carbon (DLC) films subjected to particle impact using MD simulation. The simulation results established the connection between the impact energy of the particle and the time-dependent deformation of DLC. In addition, MD simulations (article number 202100437) were adopted to simulate the packing of CNT bundles which were assumed to be above a flat substrate. It was found that there are many stationary states in the portion of collapsed nanotubes. By using phase-field crystal modeling and MD simulation, Chen et al. (article number 202100360) investigated coiled CNTs with various geometries to examine their mechanical and thermal properties. It was found that the Young's modulus increases, while the thermal conductivity decreases with the increase of coil pitch and CNT radius. Besides thermal conduction, CNTs bundles were explored by MD simulation to show a negative thermal expansion up to a temperature of 1500 K (article number 202100415). Such behavior was attributed to the elliptization of the CNT cross section and thermal-fluctuation induced bending. Quantum simulations based on DFT allow the prediction of the catalytic properties of novel materials, which facilitates the design of new structures with premier activity and the identification of their activation center for promoted catalysis. You et al. (article number 202100344) found that Pd-based Janus transition metal dichalcogenides (TMDs) possess suitable band edge positions allowing the redox reaction of water, which is crucial for hydrogen and oxygen evolution reactions. The appropriate band gaps allow an efficient harvesting of ultraviolet and visible photons for water-splitting photocatalysts. Similar analysis was performed in Cr-, Mo-, and W-based Janus TMDs by Liu et al. (article number 202100417). It was found that 2D CrSSe is particularly promising due to its excellent visible infrared light absorption capability, and high solar-to-hydrogen efficiency up to 30%. The rich 2D materials permit the construction of various possible van der Waals hybrids through stacking different TMD layers. DFT calculations provide a fast screening of various combinations of these TMD layers by characterizing their band structures and interlayer binding energies. A T-type WTe2/MoS2 heterostructure was investigated via DFT calculation to obtain the electronic properties (article number 202100444). It was reported that the electronic probability and electronic dispersion can be determined based on electronic entropy. Based on first-principles calculations, Shang et al. (article number 202100203) found that the 2D phosphorus carbide (PC5) monolayer is similar to graphene and possesses an intrinsic Dirac cone structure which is very robust against external biaxial and uniaxial strains. The stability of this new phosphorus carbide compound was confirmed by calculating its formation energy, phonon curves, and mechanical elastic constants. First-principles calculations are also able to study fracture modes and superplastic deformation of nanomaterials. For instance, the mechanical response of layered InSe crystal structure was calculated by DFT (article number 202100418) and its uniaxial tensile deformation was found to depend on the loading directions, with brittle fracture being observed in the in-plane [100] and [110] directions and ductile failure in the out-of-plane [001] direction. This phenomenon was ascribed to the different atomic diffusing pathways including interlayer tangling, amorphization, and crosslinking along these deformation directions. The high-dimensional input and output data from modelling and simulation can be fed into the ML model with further classification and cluster analysis which is helpful for the discovery of new materials and prediction of their properties. To identify the features of high-energy density materials, Bondarev et al. (article number 202100191) used neural networks to find the dependencies between first-principles calculations and experimental data of releasing heat. The above-mentioned works are recent progress of simulations of various 1D and 2D nanomaterials with the topics ranging from mechanical behaviors, electronic properties, chemical catalysis, etc. As Guest Editors of this Special Issue, we cordially thank all the authors of these works for their great contributions. Kun Zhou is an associate professor in the School of Mechanical and Aerospace Engineering at Nanyang Technological University (NTU), Singapore. He received his Bachelor's and Master's degrees at Tsinghua University, China in 1998 and 2001, respectively and his Ph.D. at NTU, Singapore in 2006. He worked as a Postdoctoral Fellow at the Center for Surface Engineering & Tribology, Northwestern University, USA from 2007 to 2010. His research interests include mechanics of materials, modeling and simulation, and additive manufacturing. Bo Liu is an associate professor in the College of Mechanical & Vehicle Engineering at Hunan University, China. He received his Bachelor's and Master's degrees at Harbin Institute of Technology, China in 2009 and 2011, respectively and his Ph.D. at Nanyang Technological University, Singapore in 2015. He worked as a Postdoctoral Fellow at Nanyang Environmental and Water Research Institute, Singapore from 2015 to 2017. His research interests involve molecular dynamics simulation of heat and mass transport in low-dimensional functional nanomaterials and related nanostructures. Yongqing Cai is an assistant professor in the Institute of Applied Physics and Materials Engineering at University of Macau, China. He received his Bachelor's degree in 2004 at Northwestern Polytechnical University, China and his Ph.D. at National University of Singapore. He worked at the Institute of High Performance Computing, Singapore from 2013 to 2019. His research interests involve first-principles simulation and materials informatics. Sergey V. Dimtriev defended his theses for the degree of candidate of technical sciences at the Tver State University in 1987 and for the degree of doctor of physical and mathematical sciences at the Altai State Technical University in 2008. He worked at the University of Electrocommunications, Japan and then in the Institute of Industrial Science at the Tokyo State University from 1995 to 2007. He headed a laboratory in the Institute for Metals Superplasticity Problems of the Russian Academy of Sciences from 2008 to 2021. Currently, he is the head of the group “Nonlinear Dynamics of Crystals” in the Institute of Molecule and Crystal Physics of the Ufa Federal Research Center of the Russian Academy of Sciences. Shaofan Li is a professor of Applied and Computational Mechanics at the University of California, Berkeley, USA. He received his Bachelor's degree in mechanical engineering at the East China University of Science and Technology in 1982 and his Ph.D. at Northwestern University, USA in 1997. He joined the University of California, Berkeley in 2000 as a faculty member. One of his current research focuses is atomistic and multiscale modeling and simulations of nanomaterials and their mechanical responses. Nanomaterials ; Nanotechnology ; Materials science ; Computer science Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Diamond and Carbon-based Materials Research