Gap prediction in hybrid graphene - hexagonal boron nitride nanoflakes using artificial neural networks

The electronic properties graphene nanoflakes (GNFs) with embedded hexagonal boron nitride (hBN) domains are investigated by combined {\it ab initio} density functional theory calculations and machine learning techniques. The energy gaps of the quasi-0D graphene based systems, defined as the differences between LUMO and HOMO energies, depend on the sizes of the hBN domains relative to the size of the pristine graphene nanoflake, but also on the position of the hBN domain. The range of the energy gaps for different configurations is increasing as the hBN domains get larger. We develop two artificial neural network (ANN) models able to reproduce the gap energies with high accuracies and investigate the tunability of the energy gap, by considering a set of GNFs with embedded rectangular hBN domains. In one ANN model, the input is in one-to-one correspondence with the atoms in the GNF, while in the second model the inputs account for basic structures in the GNF, allowing potential use in up-scaled structures. We perform a statistical analysis over different configurations of ANNs to optimize the network structure. The trained ANNs provide a correlation between the atomic system configuration and the magnitude of the energy gaps, which may be regarded as an efficient tool for optimizing the design of nanostructured graphene based materials for specific electronic properties.Comment: 6 pages, 5 figure

A Laboratory Investigation of Supersonic Clumpy Flows: Experimental Design and Theoretical Analysis

We present a design for high energy density laboratory experiments studying the interaction of hypersonic shocks with a large number of inhomogeneities. These ``clumpy'' flows are relevant to a wide variety of astrophysical environments including the evolution of molecular clouds, outflows from young stars, Planetary Nebulae and Active Galactic Nuclei. The experiment consists of a strong shock (driven by a pulsed power machine or a high intensity laser) impinging on a region of randomly placed plastic rods. We discuss the goals of the specific design and how they are met by specific choices of target components. An adaptive mesh refinement hydrodynamic code is used to analyze the design and establish a predictive baseline for the experiments. The simulations confirm the effectiveness of the design in terms of articulating the differences between shocks propagating through smooth and clumpy environments. In particular, we find significant differences between the shock propagation speeds in a clumpy medium compared to a smooth one with the same average density. The simulation results are of general interest for foams in both inertial confinement fusion and laboratory astrophysics studies. Our results highlight the danger of using average properties of inhomogeneous astrophysical environments when comparing timescales for critical processes such as shock crossing and gravitational collapse times.Comment: 7 pages, 6 figures. Submitted to the Astrophysical Journal. For additional information, including simulation animations and the pdf and ps files of the paper with embedded high-quality images, see http://pas.rochester.edu/~wm

Designing Plasmon-Enhanced Thermochromic Films Using a Vanadium Dioxide Nanoparticle Elastomeric Composite

Vanadium dioxide (VO2) is a common material for use in thermochromic windows due to a semiconductor-to-metal transition (SMT) that is coupled with a change in infrared opacity. Commercialization of VO2-based thermochromic technology is hampered by relatively expensive synthesis and film fabrication techniques as well as overall low performance as a window material. Here, simulations that indicate the plasmon resonance of VO2 nanoparticles in a composite film, which can be tuned to achieve record performance values, are reported. These simulations are experimentally verified by fabricating a VO2 nanoparticle composite in an elastomeric matrix using low-temperature and atmospheric processing conditions. The optical properties of the films are analyzed, yielding visible transmittance and infrared modulation values within the range of top-performing thermochromic windows. In addition, an improvement in performance is observed upon stretching the films, an effect that can be attributed to a local refractive index modulation. The results highlight the potential use of elastomeric composites as a low-cost route to higher-performance smart windows

Gap Prediction in Hybrid Graphene-Hexagonal Boron Nitride Nanoflakes Using Artificial Neural Networks

The electronic properties of graphene nanoflakes (GNFs) with embedded hexagonal boron nitride (hBN) domains are investigated by combined ab initio density functional theory calculations and machine-learning techniques. The energy gaps of the quasi-0D graphene-based systems, defined as the differences between LUMO and HOMO energies, depend not only on the sizes of the hBN domains relative to the size of the pristine graphene nanoflake but also on the position of the hBN domain. The range of the energy gaps for different configurations increases as the hBN domains get larger. We develop two artificial neural network (ANN) models able to reproduce the gap energies with high accuracies and investigate the tunability of the energy gap, by considering a set of GNFs with embedded rectangular hBN domains. In one ANN model, the input is in one-to-one correspondence with the atoms in the GNF, while in the second model the inputs account for basic structures in the GNF, allowing potential use in upscaled systems. We perform a statistical analysis over different configurations of ANNs to optimize the network structure. The trained ANNs provide a correlation between the atomic system configuration and the magnitude of the energy gaps, which may be regarded as an efficient tool for optimizing the design of nanostructured graphene-based materials for specific electronic properties