GPU acceleration of local and semilocal density functional calculations in the SPARC electronic structure code.

We present a Graphics Processing Unit (GPU)-accelerated version of the real-space SPARC electronic structure code for performing Kohn-Sham density functional theory calculations within the local density and generalized gradient approximations. In particular, we develop a modular math-kernel based implementation for NVIDIA architectures wherein the computationally expensive operations are carried out on the GPUs, with the remainder of the workload retained on the central processing units (CPUs). Using representative bulk and slab examples, we show that relative to CPU-only execution, GPUs enable speedups of up to 6× and 60× in node and core hours, respectively, bringing time to solution down to less than 30 s for a metallic system with over 14 000 electrons and enabling significant reductions in computational resources required for a given wall time.

Compact MZI modulators on thin film Z-cut lithium niobate.

In this paper, we designed, implemented, and characterized compact Mach-Zehnder interferometer-based electro-optic modulators. The modulator utilizes spiral-shaped optical waveguides on Z-cut lithium niobate and the preeminent electro-optic effect which is applied using top and bottom electrodes. Optical waveguides are made of rib etched lithium niobate waveguides with bottom silicon oxide cladding, while SU8 polymer covers the top and sides of the rib waveguides. The proposed implementation resulted in low optical losses < 1.3 dB/cm. Moreover, we achieved compact modulators that fit 0.286 cm and 2 cm long optical waveguides in 110 µm × 110 µm and 300 µm × 300 µm areas, respectively. For single arm modulation, the modulators achieved a VπL of 7.4 V.cm and 6.4 V.cm and 3-dB bandwidths of 9.3 GHz and 2.05 GHz, respectively. Push-pull modulation is expected to cut these VπL in half. The proposed configuration avoids traveling wave modulation complexities and represents a key development towards miniature and highly integrated photonic circuits.

Highly linear lithium niobate Michelson interferometer modulators assisted by spiral Bragg grating reflectors.

Highly linear electro-optic modulators are key components in analog microwave photonic links, offering on-chip direct mixing of optical and RF fields. In this work, we demonstrate a monolithic integrated Michelson interferometer modulator on thin-film lithium niobate (LN), that achieves linearized performance by modulating Bragg grating reflectors placed at the end of Michelson arms. The modulator utilizes spiral-shaped waveguide Bragg gratings on Z-cut LN with top and bottom electrodes to realize extensive reflectors, essential for linearized performance, in a highly integrated form. Optical waveguides are realized using rib etching of LN with precisely engineered bottom and top cladding layers made of silicon dioxide and SU-8 polymer, respectively. The compact design fits a 3 mm long grating in an 80 µm × 80 µm area, achieving a broad operating bandwidth up to 18 GHz. A spurious free dynamic range (SFDR) of 101.2 dB·Hz2/3 is demonstrated at 1 GHz, compared to 91.5 dB·Hz2/3 for a reference Mach-Zehnder modulator fabricated on the same chip. Further enhancement in SFDR could be achieved by reducing fiber-to-chip coupling loss. The proposed demonstration could significantly improve the linearity of analog modulator-based integrated optical links.

A linear scaling hierarchical block low-rank representation of the electron repulsion integral tensor.

Efficient representations of the electron repulsion integral (ERI) tensor and fast algorithms for contractions with the ERI tensor often employ a low-rank approximation of the tensor or its sub-blocks. Such representations include density fitting (DF), the continuous fast multipole method (CFMM), and, more recently, hierarchical matrices. We apply the H2 hierarchical matrix representation to the ERI tensor with Gaussian basis sets to rapidly calculate the Coulomb matrices in Hartree-Fock and density functional theory calculations. The execution time and storage requirements of the hierarchical matrix approach and the DF approach are compared. The hierarchical matrix approach has very modest storage requirements, allowing large calculations to be performed in memory without recomputing ERIs. We interpret the hierarchical matrix approach as a multilevel, localized DF method and also discuss the close relationship between the hierarchical matrix approaches with CFMM. Like CFMM, the hierarchical matrix approach is asymptotically linear scaling, but the latter requires severalfold less memory (or severalfold less computation, if quantities are computed dynamically) due to being able to efficiently employ low-rank approximations for far more blocks.

Techniques for high-performance construction of Fock matrices.

This paper presents techniques for Fock matrix construction that are designed for high performance on shared and distributed memory parallel computers when using Gaussian basis sets. Four main techniques are considered. (1) To calculate electron repulsion integrals, we demonstrate batching together the calculation of multiple shell quartets of the same angular momentum class so that the calculation of large sets of primitive integrals can be efficiently vectorized. (2) For multithreaded summation of entries into the Fock matrix, we investigate using a combination of atomic operations and thread-local copies of the Fock matrix. (3) For distributed memory parallel computers, we present a globally accessible matrix class for accessing distributed Fock and density matrices. The new matrix class introduces a batched mode for remote memory access that can reduce the synchronization cost. (4) For density fitting, we exploit both symmetry (of the Coulomb and exchange matrices) and sparsity (of 3-index tensors) and give a performance comparison of density fitting and the conventional direct calculation approach. The techniques are implemented in an open-source software library called GTFock.

Effects of confinement on models of intracellular macromolecular dynamics.

The motions of particles in a viscous fluid confined within a spherical cell have been simulated using Brownian and Stokesian dynamics simulations. High volume fractions mimicking the crowded interior of biological cells were used. Importantly, although confinement yields an overall slowdown in motion, the qualitative effects of motion in the interior of the cell can be effectively modeled as if the system were an infinite periodic system. However, we observe layering of particles at the cell wall due to steric interactions in the confined space. Motions of nearby particles are also strongly correlated at the cell wall, and these correlations increase when hydrodynamic interactions are modeled. Further, particles near the cell wall have a tendency to remain near the cell wall. A consequence of these effects is that the mean contact time between particles is longer at the cell wall than in the interior of the cell. These findings identify a specific way that confinement affects the interactions between particles and points to a previously unidentified mechanism that may play a role in signal transduction and other processes near the membrane of biological cells.

DNA Internal Motion Likely Accelerates Protein Target Search in a Packed Nucleoid.

Transcription factors must diffuse through densely packed and coiled DNA to find their binding sites. Using a coarse-grained model of DNA and lac repressor (LacI) in the Escherichia coli nucleoid, simulations were performed to examine how LacI diffuses in such a space. Despite the canonical picture of LacI diffusing rather freely, in reality the DNA is densely packed, is not rigid but highly mobile, and the dynamics of DNA dictates to a great extent the LacI motion. A possibly better picture of unbound LacI motion is that of gated diffusion, where DNA confines LacI in a cage, but LacI can move between cages when hindering DNA strands move out of the way. Three-dimensional diffusion constants for unbound LacI computed from simulations closely match those for unbound LacI in vivo reported in the literature. The internal motions of DNA appear to be governed by strong internal forces arising from being crowded into the small space of the nucleoid. A consequence of the DNA internal motion is that protein target search may be accelerated.

Horizontal vectorization of electron repulsion integrals.

We present an efficient implementation of the Obara-Saika algorithm for the computation of electron repulsion integrals that utilizes vector intrinsics to calculate several primitive integrals concurrently in a SIMD vector. Initial benchmarks display a 2-4 times speedup with AVX instructions over comparable scalar code, depending on the basis set. Speedup over scalar code is found to be sensitive to the level of contraction of the basis set, and is best for (lAlB|lClD) quartets when lD = 0 or lB=lD=0, which makes such a vectorization scheme particularly suitable for density fitting. The basic Obara-Saika algorithm, how it is vectorized, and the performance bottlenecks are analyzed and discussed. © 2016 Wiley Periodicals, Inc.

Parallel scalability of Hartree-Fock calculations.

Quantum chemistry is increasingly performed using large cluster computers consisting of multiple interconnected nodes. For a fixed molecular problem, the efficiency of a calculation usually decreases as more nodes are used, due to the cost of communication between the nodes. This paper empirically investigates the parallel scalability of Hartree-Fock calculations. The construction of the Fock matrix and the density matrix calculation are analyzed separately. For the former, we use a parallelization of Fock matrix construction based on a static partitioning of work followed by a work stealing phase. For the latter, we use density matrix purification from the linear scaling methods literature, but without using sparsity. When using large numbers of nodes for moderately sized problems, density matrix computations are network-bandwidth bound, making purification methods potentially faster than eigendecomposition methods.

Single nanoparticle detection using photonic crystal enhanced microscopy.

We demonstrate a label-free biosensor imaging approach that utilizes a photonic crystal (PC) surface to detect surface attachment of individual dielectric and metal nanoparticles through measurement of localized shifts in the resonant wavelength and resonant reflection magnitude from the PC. Using a microscopy-based approach to scan the PC resonant reflection properties with 0.6 µm spatial resolution, we show that metal nanoparticles attached to the biosensor surface with strong absorption at the resonant wavelength induce a highly localized reduction in reflection efficiency and are able to be detected by modulation of the resonant wavelength. Experimental demonstrations of single-nanoparticle imaging are supported by finite-difference time-domain computer simulations. The ability to image surface-adsorption of individual nanoparticles offers a route to single molecule biosensing, in which the particles can be functionalized with specific recognition molecules and utilized as tags.

Dynamic simulation of concentrated macromolecular solutions with screened long-range hydrodynamic interactions: algorithm and limitations.

Hydrodynamic interactions exert a critical effect on the dynamics of macromolecules. As the concentration of macromolecules increases, by analogy to the behavior of semidilute polymer solutions or the flow in porous media, one might expect hydrodynamic screening to occur. Hydrodynamic screening would have implications both for the understanding of macromolecular dynamics as well as practical implications for the simulation of concentrated macromolecular solutions, e.g., in cells. Stokesian dynamics (SD) is one of the most accurate methods for simulating the motions of N particles suspended in a viscous fluid at low Reynolds number, in that it considers both far-field and near-field hydrodynamic interactions. This algorithm traditionally involves an O(N(3)) operation to compute Brownian forces at each time step, although asymptotically faster but more complex SD methods are now available. Motivated by the idea of hydrodynamic screening, the far-field part of the hydrodynamic matrix in SD may be approximated by a diagonal matrix, which is equivalent to assuming that long range hydrodynamic interactions are completely screened. This approximation allows sparse matrix methods to be used, which can reduce the apparent computational scaling to O(N). Previously there were several simulation studies using this approximation for monodisperse suspensions. Here, we employ newly designed preconditioned iterative methods for both the computation of Brownian forces and the solution of linear systems, and consider the validity of this approximation in polydisperse suspensions. We evaluate the accuracy of the diagonal approximation method using an intracellular-like suspension. The diffusivities of particles obtained with this approximation are close to those with the original method. However, this approximation underestimates intermolecular correlated motions, which is a trade-off between accuracy and computing efficiency. The new method makes it possible to perform large-scale and long-time simulation with an approximate accounting of hydrodynamic interactions.

Application of plasmonic bowtie nanoantenna arrays for optical trapping, stacking, and sorting.

We present the use of Au bowtie nanoantenna arrays (BNAs) for highly efficient, multipurpose particle manipulation with unprecedented low input power and low-numerical aperture (NA) focusing. Optical trapping efficiencies measured are up to 20× the efficiencies of conventional high-NA optical traps and are among the highest reported to date. Empirically obtained plasmonic optical trapping "phase diagrams" are introduced to detail the trapping response of the BNAs as a function of input power, wavelength, polarization, particle diameter, and BNA array spacing (number density). Using these diagrams, parameters are chosen, employing strictly the degrees-of-freedom of the input light, to engineer specific trapping tasks including (1) dexterous, single-particle trapping and manipulation, (2) trapping and manipulation of two- and three-dimensional particle clusters, and (3) particle sorting. The use of low input power densities (power and NA) suggests that this bowtie nanoantenna trapping system will be particularly attractive for lab-on-a-chip technology or biological applications aimed at reducing specimen photodamage.

Transport in nanoribbon interconnects obtained from graphene grown by chemical vapor deposition.

We study graphene nanoribbon (GNR) interconnects obtained from graphene grown by chemical vapor deposition (CVD). We report low- and high-field electrical measurements over a wide temperature range, from 1.7 to 900 K. Room temperature mobilities range from 100 to 500 cm(2)·V(-1)·s(-1), comparable to GNRs from exfoliated graphene, suggesting that bulk defects or grain boundaries play little role in devices smaller than the CVD graphene crystallite size. At high-field, peak current densities are limited by Joule heating, but a small amount of thermal engineering allows us to reach ∼2 × 10(9) A/cm(2), the highest reported for nanoscale CVD graphene interconnects. At temperatures below ∼5 K, short GNRs act as quantum dots with dimensions comparable to their lengths, highlighting the role of metal contacts in limiting transport. Our study illustrates opportunities for CVD-grown GNRs, while revealing variability and contacts as remaining future challenges.

Krylov subspace methods for computing hydrodynamic interactions in brownian dynamics simulations.

Hydrodynamic interactions play an important role in the dynamics of macromolecules. The most common way to take into account hydrodynamic effects in molecular simulations is in the context of a brownian dynamics simulation. However, the calculation of correlated brownian noise vectors in these simulations is computationally very demanding and alternative methods are desirable. This paper studies methods based on Krylov subspaces for computing brownian noise vectors. These methods are related to Chebyshev polynomial approximations, but do not require eigenvalue estimates. We show that only low accuracy is required in the brownian noise vectors to accurately compute values of dynamic and static properties of polymer and monodisperse suspension models. With this level of accuracy, the computational time of Krylov subspace methods scales very nearly as O(N(2)) for the number of particles N up to 10 000, which was the limit tested. The performance of the Krylov subspace methods, especially the "block" version, is slightly better than that of the Chebyshev method, even without taking into account the additional cost of eigenvalue estimates required by the latter. Furthermore, at N = 10,000, the Krylov subspace method is 13 times faster than the exact Cholesky method. Thus, Krylov subspace methods are recommended for performing large-scale brownian dynamics simulations with hydrodynamic interactions.

Pseudodiagonalization Method for Accelerating Nonlinear Subspace Diagonalization in Density Functional Theory.

In density functional theory, each self-consistent field (SCF) nonlinear step updates the discretized Kohn-Sham orbitals by solving a linear eigenvalue problem. The concept of pseudodiagonalization is to solve this linear eigenvalue problem approximately and specifically utilizing a method involving a small number of Jacobi rotations that takes advantage of the good initial guess to the solution given by the approximation to the orbitals from the previous SCF iteration. The approximate solution to the linear eigenvalue problem can be very rapid, particularly for those steps near SCF convergence. We adapt pseudodiagonalization to finite-temperature and metallic systems, where partially occupied orbitals must be individually resolved with some accuracy. We apply pseudodiagonalization to the subspace eigenvalue problem that arises in Chebyshev-filtered subspace iteration. In tests on metallic and other systems for a range of temperatures, we show that pseudodiagonalization achieves similar rates of SCF convergence to exact diagonalization.

High-k dielectric Al2O3 nanowire and nanoplate field effect sensors for improved pH sensing.

Over the last decade, field-effect transistors (FETs) with nanoscale dimensions have emerged as possible label-free biological and chemical sensors capable of highly sensitive detection of various entities and processes. While significant progress has been made towards improving their sensitivity, much is yet to be explored in the study of various critical parameters, such as the choice of a sensing dielectric, the choice of applied front and back gate biases, the design of the device dimensions, and many others. In this work, we present a process to fabricate nanowire and nanoplate FETs with Al(2)O(3) gate dielectrics and we compare these devices with FETs with SiO(2) gate dielectrics. The use of a high-k dielectric such as Al(2)O(3) allows for the physical thickness of the gate dielectric to be thicker without losing sensitivity to charge, which then reduces leakage currents and results in devices that are highly robust in fluid. This optimized process results in devices stable for up to 8 h in fluidic environments. Using pH sensing as a benchmark, we show the importance of optimizing the device bias, particularly the back gate bias which modulates the effective channel thickness. We also demonstrate that devices with Al(2)O(3) gate dielectrics exhibit superior sensitivity to pH when compared to devices with SiO(2) gate dielectrics. Finally, we show that when the effective electrical silicon channel thickness is on the order of the Debye length, device response to pH is virtually independent of device width. These silicon FET sensors could become integral components of future silicon based Lab on Chip systems.

Leaky-mode assisted fluorescence extraction: application to fluorescence enhancement biosensors.

Efficient recovery of light emitted by fluorescent molecules by employing photonic structures can result in high signal-to-noise ratio detection for biological applications including DNA microarrays, fluorescence microscopy and single molecule detection. By employing a model system comprised of colloidal quantum dots, we consider the physical basis of the extraction effect as provided by photonic crystals. Devices with different lattice symmetry are fabricated ensuring spectral and spatial coupling of quantum dot emission with leaky eigenmodes and the emission characteristics are studied using angle-resolved and angle-integrated measurements. Comparison with numerical calculations and lifetime measurements reveals that the enhancement occurs via resonant redirection of the emitted radiation. Comparison of various lattices reveals differences in the enhancement factor with a maximum enhancement factor approaching 220. We also demonstrate the first enhanced extraction biosensor that allows for over 20-fold enhancement of the fluorescence signal in detection of the cytokine TNF-alpha by a fluorescence sandwich immunoassay.

An optofluidic metasurface for lateral flow-through detection of breast cancer biomarker.

The rapid growth of point-of-care tests demands for biosensors with high sensitivity and small size. This paper demonstrates an optofluidic metasurface that combines silicon-on-insulator (SOI) nanophotonics and nanofluidics to realize a high-performance, lateral flow-through biosensor. The metasurface is made of a periodic array of silicon nanoposts on an SOI substrate, and functionalized with specific receptor molecules. Bonding of a polydimethylsiloxane slab directly onto the surface results in an ultracompact biosensor, where analyte solutions are restricted to flow only in the space between the nanoposts. No flow exists above the nanoposts. This sensor design overcomes the issue with diffusion-limited detection of many other biosensors. The lateral flow-through feature, in conjunction with high-Q resonance modes associated with optical bound states of the metasurface, offers an improved sensitivity to subtle molecule-bonding induced changes in refractive index. The device exhibits a resonance mode around 1550 nm wavelength and provides an index sensitivity of 720 nm/RIU. Biosensing is conducted to detect the epidermal growth factor receptor 2 (ErbB2), a protein biomarker for early-stage breast cancer screening, by monitoring resonance wavelength shifts in response to specific analyte-ligand binding events at the metasurface. The limit of detection of the device is 0.7 ng mL-1 for ErbB2.

Material Synthesis and Device Aspects of Monolayer Tungsten Diselenide.

In this paper, we investigate the synthesis of WSe2 by chemical vapor deposition and study the current transport and device scaling of monolayer WSe2. We found that the device characteristics of the back-gated WSe2 transistors with thick oxides are very sensitive to the applied drain bias, especially for transistors in the sub-micrometer regime. The threshold voltage, subthreshold swing, and extracted field-effect mobility vary with the applied drain bias. The output characteristics in the long-channel transistors show ohmic-like behavior, while that in the short-channel transistors show Schottky-like behavior. Our investigation reveals that these phenomena are caused by the drain-induced barrier lowering (short-channel effect). For back-gated WSe2 transistors with 280 nm oxide, the short-channel effect appears when the channel length is shorter than 0.4 µm. This extremely long electrostatic scaling length is due to the thick back-gate oxides. In addition, we also found that the hydrogen flow rate and the amount of WO3 precursor play an important role in the morphology of the WSe2. The hole mobility of the monolayer WSe2 is limited by Columbic scattering below 250 K, while it is limited by phonon scattering above 250 K. These findings are very important for the synthesis of WSe2 and accurate characterization of the electronic devices based on 2D materials.

Tunable Optical Nanoantennas Incorporating Bowtie Nanoantenna Arrays with Stimuli-Responsive Polymer.

We report on a temperature-responsive tunable plasmonic device that incorporates coupled bowtie nanoantenna arrays (BNAs) with a submicron-thick, thermosensitive hydrogel coating. The coupled plasmonic nanoparticles provide an intrinsically higher field enhancement than conventional individual nanoparticles. The favorable scaling of plasmonic dimers at the nanometer scale and ionic diffusion at the submicron scale is leveraged to achieve strong optical resonance and rapid hydrogel response, respectively. We demonstrate that the hydrogel-coated BNAs are able to sense environmental temperature variations. The phase transition of hydrogel leads to 16.2 nm of resonant wavelength shift for the hydrogel-coated BNAs, whereas only 3 nm for the uncoated counterpart. The response time of the device to temperature variations is only 250 ms, due to the small hydrogel thickness at the submicron scale. The demonstration of the ability of the device to tune its optical resonance in response to an environmental stimulus (here, temperature) suggests a possibility of making many other tunable plasmonic devices through the incorporation of coupled plasmonic nanostructures and various environmental-responsive hydrogels.