Data-driven acceleration of Photonic Simulations

Designing modern photonic devices often involves traversing a large parameter space via an optimization procedure, gradient based or otherwise, and typically results in the designer performing electromagnetic simulations of correlated devices. In this paper, we present an approach to accelerate the Generalized Minimal Residual (GMRES) algorithm for the solution of frequency-domain Maxwell's equations using two machine learning models (principal component analysis and a convolutional neural network) trained on simulations of correlated devices. These data-driven models are trained to predict a subspace within which the solution of the frequency-domain Maxwell's equations lie. This subspace can then be used for augmenting the Krylov subspace generated during the GMRES iterations. By training the proposed models on a dataset of grating wavelength-splitting devices, we show an order of magnitude reduction ($\sim 10 - 50$) in the number of GMRES iterations required for solving frequency-domain Maxwell's equations

Spin-charge gauge approach to metal-insulator crossover and transport properties in High-Tc_c cuprates

The spin-charge gauge approach to consider the metal-insulator crossover (MIC) and other anomalous transport properties in High-T$_c$ cuprates is briefly reviewed. A U(1) field gauging the global charge symmetry and an SU(2) field gauging the global spin-rotational symmetry are introduced to study the two-dimensional $t-J$ model in the limit $t\gg J$. The MIC as a clue to the understanding of the ``pseudogap'' (PG) phase, is attributed to the competition between the short-range antiferromagnetic order and dissipative motion of charge carriers coupled to the slave-particle gauge field. The composite particle formed by binding the charge carrier (holon) and spin excitation (spinon) via the slave particle gauge field exhibits a number of peculiar properties, and the calculated results are in good agreement with experimental data for both PG and ``strange metal'' phases. Connections to other gauge field approaches in studying the strong correlation problem are also briefly outlined.Comment: 32 pages, to appear in the special issue on "Correlated Electrons" of J. Phys.: Condens. Mat

A compact photomicroreactor design for kinetic studies of gas-liquid photocatalytic transformations

A compact photomicroreactor assembly consisting of a capillary microreactor and small-scale LEDs was developed for the study of reaction kinetics in the gas-liquid photocatalytic oxidation of thiophenol to phenyl disulfide within Taylor flow. The importance of photons was convincingly shown by a suction phenomenon due to the fast consumption of oxygen. Mass transfer limitations were evaluated and an operational zone without mass transfer effects was chosen to study reaction kinetics. Effects of photocatalyst loading and light sources on the reaction performance were investigated. Reaction kinetic analysis was performed to obtain reaction orders with respect to both thiophenol and oxygen based on heterogeneous and homogeneous experimental results, respectively. The Hatta number further indicated elimination of mass transfer limitations. Reaction rate constants at different photocatalyst loadings and different photon flux were calculated. Furthermore, the advantages of this photomicroreactor assembly for studying gas-liquid photocatalytic reaction kinetics were demonstrated as compared with batch reactors. This article is protected by copyright. All rights reserved

Microbial regulation of the soil carbon cycle: evidence from gene-enzyme relationships.

A lack of empirical evidence for the microbial regulation of ecosystem processes, including carbon (C) degradation, hinders our ability to develop a framework to directly incorporate the genetic composition of microbial communities in the enzyme-driven Earth system models. Herein we evaluated the linkage between microbial functional genes and extracellular enzyme activity in soil samples collected across three geographical regions of Australia. We found a strong relationship between different functional genes and their corresponding enzyme activities. This relationship was maintained after considering microbial community structure, total C and soil pH using structural equation modelling. Results showed that the variations in the activity of enzymes involved in C degradation were predicted by the functional gene abundance of the soil microbial community (R2>0.90 in all cases). Our findings provide a strong framework for improved predictions on soil C dynamics that could be achieved by adopting a gene-centric approach incorporating the abundance of functional genes into process models

A quantum Monte Carlo study of the one-dimensional ionic Hubbard model

Quantum Monte Carlo methods are used to study a quantum phase transition in a 1D Hubbard model with a staggered ionic potential (D). Using recently formulated methods, the electronic polarization and localization are determined directly from the correlated ground state wavefunction and compared to results of previous work using exact diagonalization and Hartree-Fock. We find that the model undergoes a thermodynamic transition from a band insulator (BI) to a broken-symmetry bond ordered (BO) phase as the ratio of U/D is increased. Since it is known that at D = 0 the usual Hubbard model is a Mott insulator (MI) with no long-range order, we have searched for a second transition to this state by (i) increasing U at fixed ionic potential (D) and (ii) decreasing D at fixed U. We find no transition from the BO to MI state, and we propose that the MI state in 1D is unstable to bond ordering under the addition of any finite ionic potential. In real 1D systems the symmetric MI phase is never stable and the transition is from a symmetric BI phase to a dimerized BO phase, with a metallic point at the transition