Related papers: Accessing negative Poisson`s ratio of graphene by …
Graphene nanoribbons (GNRs) are a family of one-dimensional (1D) materials carved from graphene lattice. GNRs possess high mobility and current carrying capability, sizable bandgap, and versatile electronic properties tailored by the…
The resistance to failure through tearing is a crucial mechanical property for the application of different elastomers. In this work, graphene nanoplatelets (GNPs) were introduced into a fluoroelastomer (FKM) matrix with the aim of…
Nuclear masses are machine-learned as a function of proton and neutron numbers. The neural network with additive Gaussian process regression-optimized activation functions (GPR-NN) method is employed for the first time for this purpose.…
The electronic nonlinear transport through ultra narrow graphene nanoribbons (sub-$10nm$) is studied. A stable region of negative differential resistance (NDR) appears in the I-V characteristic curve of {\it odd} zigzag graphene nanoribbons…
Neutral to the right (NTR) processes were introduced by Doksum in 1974 as Bayesian priors on the class of distributions on the real line. Since that time there have been numerous applications to models that arise in survival analysis…
We handshake statistical mechanics with continuum mechanics to develop a methodology for consistent evaluation of the continuum scale properties of graphene. The scope is kept limited to elastic modulus, $E$, which has been reported to vary…
Analysis of chemical graphs is becoming a major research topic in computational molecular biology due to its potential applications to drug design. One of the major approaches in such a study is inverse quantitative structure…
We propose a physics-guided neural network (PGNN) framework for constructing nucleon-nucleon inverse potentials based on inverse scattering theory. The framework integrates the Phase Function Method (PFM) with a two-stage supervised…
Hexagonal boron nitride (hBN) is drawing increasing attention as an insulator and substrate material to develop next generation graphene-based electronic devices. In this paper, we investigate the quantum transport in heterostructures…
Particle-based modeling of materials at atomic scale plays an important role in the development of new materials and understanding of their properties. The accuracy of particle simulations is determined by interatomic potentials, which…
Exercising direct control over the unusual electronic structures arising from quantum confinement effects in graphene nanoribbons (GNRs) - atomically defined quasi one-dimensional (1D) strips of graphene - is intimately linked to geometric…
A material exhibiting a negative Poisson's ratio is always one of the leading topics in materials science, which is due to the potential applications in those special areas such as defence and medicine. In this letter, we demonstrate a new…
Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of such products is fundamentally different from the…
Accounting for nuclear quantum effects (NQEs) can significantly alter material properties at finite temperatures. Atomic modeling using the path-integral molecular dynamics (PIMD) method can fully account for such effects, but requires…
Combining the efficiency of semi-empirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and…
Recently graphene was introduced with tunable ripple texturing, a nanofabric enabled by graphene's remarkable elastic properties. However, one can further envision sandwiching the ripples, thus constructing composite nanomaterial, graphene…
Graphene nanoribbons with armchair edges (AGNRs) have bandgaps that can be flexibly tuned via the ribbon width. A junction made of a narrower AGNR sandwiched between two wider AGNR leads was recently reported to possess two perfect…
Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have made considerable progress in…
Defect engineering via parallel cracks has been proposed as a route to tailor the fracture response of graphene. However, atomistic fracture predictions can be strongly sensitive to the interatomic potential. Here, we quantify the effect of…
Auxetic materials (materials with negative Poisson's ratio) and nanomaterials have independently been for many years two of the most active research fields in material science. Recently, these formerly independent fields have begun to…