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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Sequence polymorphism in an insect RNA virus field population: A snapshot from a single point in space and time reveals stochastic differences among and within individual hosts

Population structure of Homalodisca coagulata Virus-1 (HoCV-1) among and within field-collected insects sampled from a single point in space and time was examined. Polymorphism in complete consensus sequences among single-insect isolates was dominated by synonymous substitutions. The mutant spectrum of the C2 helicase region within each single-insect isolate was unique and dominated by nonsynonymous singletons. Bootstrapping was used to correct the within-isolate nonsynonymous:synonymous arithmetic ratio (N:S) for RT-PCR error, yielding an N:S value ~one log-unit greater than that of consensus sequences. Probability of all possible single-base substitutions for the C2 region predicted N:S values within 95% confidence limits of the corrected within-isolate N:S when the only constraint imposed was viral polymerase error bias for transitions over transversions. These results indicate that bottlenecks coupled with strong negative/purifying selection drive consensus sequences toward neutral sequence space, and that most polymorphism within single-insect isolates is composed of newly-minted mutations sampled prior to selection. -- Highlights: •Sampling protocol minimized differential selection/history among isolates. •Polymorphism among consensus sequences dominated by negative/purifying selection. •Within-isolate N:S ratio corrected for RT-PCR error by bootstrapping. •Within-isolate mutant spectrum dominated by new mutations yet to undergo selection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Quantum Perturbation Theory Using Tensor Cores and a Deep Neural Network

In this work, time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e., dense matrix–matrix multiplications, in mixed-precision arithmetics, which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree–Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low-precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Advanced Polymer Characterization: Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry encodes structural information across diverse homo- and copolymer ensembles, yet decrypting these spectra requires a systematic analytical approach. We introduce Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)─a general cipher algorithm that applies modular arithmetic to filter monomer-derived mass contributions and cluster MALDI peaks by nonconstitutional repeating units (non-CRUs). MOSAIC performs sequential modular operations using monomer mass differences as base units to compress complex spectral data, revealing end-group distributions and comonomer incorporation. As a demonstration, we applied MOSAIC to five copolymers formed by two different polymerization mechanisms. Furthermore, the resulting remainder–mass plots clearly resolve polymer homologs with distinct non-CRUs into visually apparent clusters, enabling intuitive assignment of mass spectral features.

Wang, Hanlin M. [University of Illinois at Urbana−↗
DOE OSTI2024

Quantum-parallel vectorized data encodings and computations on trapped-ion and transmon QPUs

Compact data representations in quantum systems are crucial for the development of quantum algorithms for data analysis. In this study, we present two innovative data encoding techniques, known as QCrank and QBArt, which exhibit significant quantum parallelism via uniformly controlled rotation gates. The QCrank method encodes a series of real-valued data as rotations on data qubits, resulting in increased storage capacity. On the other hand, QBArt directly incorporates a binary representation of the data within the computational basis, requiring fewer quantum measurements and enabling well-established arithmetic operations on binary data. We showcase various applications of the proposed encoding methods for various data types. Notably, we demonstrate quantum algorithms for tasks such as DNA pattern matching, Hamming weight computation, complex value conjugation, and the retrieval of a binary image with 384 pixels, all executed on the Quantinuum trapped-ion QPU. Furthermore, we employ several cloud-accessible QPUs, including those from IBMQ and IonQ, to conduct supplementary benchmarking experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Synthetic-domain computing and neural networks using lithium niobate integrated nonlinear phononics

Analogue computing uses the physical behaviours of devices to provide energy-efficient arithmetic operations. However, scaling up analogue computing platforms by simply increasing the number of devices leads to challenges such as device-to-device variation. Here, in this study, we report scalable analogue computing and neural networks in the synthetic frequency domain using an integrated nonlinear phononic platform on lithium niobate. This synthetic-domain computing is robust to device variations, as vectors and matrices are concurrently encoded at different frequencies within a single device, achieving a high throughput per area. Leveraging inherent nonlinearities, our device-aware neural network can perform a four-class classification task with an accuracy of 98.2%. The nonlinear phononic computing hardware also maintains consistent performance over a wide operational temperature range (characterized up to 192 °C). Our synthetic-domain computing combines single-device parallelism, inherent nonlinearity and environmental stability, and could be of use in edge computing applications in which power efficiency and environmental resilience are crucial.

Ji, Jun [Virginia Polytechnic Inst. and State Univ↗
DOE OSTI2017

Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Photolysis of CH{sub 3}CHO at 248 nm: Evidence of triple fragmentation from primary quantum yield of CH{sub 3} and HCO radicals and H atoms

Radical quantum yields have been measured following the 248 nm photolysis of acetaldehyde, CH{sub 3}CHO. HCO radical and H atom yields have been quantified by time resolved continuous wave Cavity Ring Down Spectroscopy in the near infrared following their conversion to HO{sub 2} radicals by reaction with O{sub 2}. The CH{sub 3} radical yield has been determined using the same technique following their conversion into CH{sub 3}O{sub 2}. Absolute yields have been deduced for HCO radicals and H atoms through fitting of time resolved HO{sub 2} profiles, obtained under various O{sub 2} concentrations, to a complex model, while the CH{sub 3} yield has been determined relative to the CH{sub 3} yield from 248 nm photolysis of CH{sub 3}I. Time resolved HO{sub 2} profiles under very low O{sub 2} concentrations suggest that another unknown HO{sub 2} forming reaction path exists in this reaction system besides the conversion of HCO radicals and H atoms by reaction with O{sub 2}. HO{sub 2} profiles can be well reproduced under a large range of experimental conditions with the following quantum yields: CH{sub 3}CHO + hν{sub 248nm} → CH{sub 3}CHO{sup *}, CH{sub 3}CHO{sup *} → CH{sub 3} + HCO ϕ{sub 1a} = 0.125 ± 0.03, CH{sub 3}CHO{sup *} → CH{sub 3} + H + CO ϕ{sub 1e} = 0.205 ± 0.04, CH{sub 3}CHO{sup *}→{sup o{sub 2}}CH{sub 3}CO + HO{sub 2} ϕ{sub 1f} = 0.07 ± 0.01. The CH{sub 3}O{sub 2} quantum yield has been determined in separate experiments as ϕ{sub CH{sub 3}} = 0.33 ± 0.03 and is in excellent agreement with the CH{sub 3} yields derived from the HO{sub 2} measurements considering that the triple fragmentation (R1e) is an important reaction path in the 248 nm photolysis of CH{sub 3}CHO. From arithmetic considerations taking into account the HO{sub 2} and CH{sub 3} measurements we deduce a remaining quantum yield for the molecular pathway: CH{sub 3}CHO{sup *} → CH{sub 4} + CO ϕ{sub 1b} = 0.6. All experiments can be consistently explained with absence of the formerly considered pathway: CH{sub 3}CHO{sup *} → CH{sub 3}CO + H ϕ{sub 1c} = 0.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

The multifacet graphically contracted function method. I. Formulation and implementation

The basic formulation for the multifacet generalization of the graphically contracted function (MFGCF) electronic structure method is presented. The analysis includes the discussion of linear dependency and redundancy of the arc factor parameters, the computation of reduced density matrices, Hamiltonian matrix construction, spin-density matrix construction, the computation of optimization gradients for single-state and state-averaged calculations, graphical wave function analysis, and the efficient computation of configuration state function and Slater determinant expansion coefficients. Timings are given for Hamiltonian matrix element and analytic optimization gradient computations for a range of model problems for full-CI Shavitt graphs, and it is observed that both the energy and the gradient computation scale as O(N{sup 2}n{sup 4}) for N electrons and n orbitals. The important arithmetic operations are within dense matrix-matrix product computational kernels, resulting in a computationally efficient procedure. An initial implementation of the method is used to present applications to several challenging chemical systems, including N{sub 2} dissociation, cubic H{sub 8} dissociation, the symmetric dissociation of H{sub 2}O, and the insertion of Be into H{sub 2}. The results are compared to the exact full-CI values and also to those of the previous single-facet GCF expansion form.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Enhancing phonon flow through one-dimensional interfaces by impedance matching

We extend concepts from microwave engineering to thermal interfaces and explore the principles of impedance matching in 1D. The extension is based on the generalization of acoustic impedance to nonlinear dispersions using the contact broadening matrix Γ(ω), extracted from the phonon self energy. For a single junction, we find that for coherent and incoherent phonons, the optimal thermal conductance occurs when the matching Γ(ω) equals the Geometric Mean of the contact broadenings. This criterion favors the transmission of both low and high frequency phonons by requiring that (1) the low frequency acoustic impedance of the junction matches that of the two contacts by minimizing the sum of interfacial resistances and (2) the cut-off frequency is near the minimum of the two contacts, thereby reducing the spillage of the states into the tunneling regime. For an ultimately scaled single atom/spring junction, the matching criterion transforms to the arithmetic mean for mass and the harmonic mean for spring constant. The matching can be further improved using a composite graded junction with an exponential varying broadening that functions like a broadband antireflection coating. There is, however, a trade off as the increased length of the interface brings in additional intrinsic sources of scattering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

What is behind small deviations of quantum mechanics theory from experiments? Observer's mathematics point of view

Certain results that have been predicted by Quantum Mechanics (QM) theory are not always supported by experiments. This defines a deep crisis in contemporary physics and, in particular, quantum mechanics. We believe that, in fact, the mathematical apparatus employed within today's physics is a possible reason. In particular, we consider the concept of infinity that exists in today's mathematics as the root cause of this problem. We have created Observer's Mathematics that offers an alternative to contemporary mathematics. This paper is an attempt to relay how Observer's Mathematics may explain some of the contradictions in QM theory results. We consider the Hamiltonian Mechanics, Newton equation, Schrodinger equation, two slit interference, wave-particle duality for single photons, uncertainty principle, Dirac equations for free electron in a setting of arithmetic, algebra, and topology provided by Observer's Mathematics (see www.mathrelativity.com). Certain results and communications pertaining to solution of these problems are provided.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Molecular simulation of fluids with non-identical intermolecular potentials: Thermodynamic properties of 10-5 + 12-6 Mie potential binary mixtures

General methods for combining interactions between particles characterised by non-identical intermolecular potentials are investigated. The combination methods are tested by performing molecular dynamics simulations to determine the pressure, energy, isochoric and isobaric heat capacities, thermal expansion coefficient, isothermal compressibility, Joule-Thomson coefficient, and speed of sound of 10-5 + 12-6 Mie potential binary mixtures. In addition to the two non-identical Mie potentials, mixtures are also studied with non-identical intermolecular parameters. The combination methods are compared with results obtained by simply averaging the Mie exponents. When either the energy or size parameters are non-identical, very significant differences emerge in the thermodynamic properties predicted by the alternative combination methods. The isobaric heat capacity is the thermodynamic property that is most affected by the relative magnitude of the intermolecular potential parameters and the method for combining non-identical potentials. Either the arithmetic or geometric combination of potentials provides a simple and effective way of performing simulations involving mixtures of components characterised by non-identical intermolecular potentials, which is independent of their functional form.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Structural properties of a bone-ceramic composite as a promising material in spinal surgery

The paper describes the results of in vitro tests of composite bone-ceramic implants and procedures for modifying implant surfaces to enhance osteogenesis. Analysis of CBCI ESs demonstrated that they have a porous structure with the mean longitudinal pore size of 70 µm and the mean transverse pore size of 46 µm; surface pores are open, while inner pores are closed. Elemental analysis of the CBCI surface demonstrates that CBCIs are composed of aluminum and zirconium oxides and contain HA inclusions. Profilometry of the CBCI ES surface revealed the following deviations: the maximum deviation of the profile in the sample center is 15 µm and 16 µm on the periphery, while the arithmetical mean and mean square deviations of the profile are 2.65 and 3.4 µm, respectively. In addition, CBCI biodegradation products were pre-examined; a 0.9% NaCl solution was used as a comparison group. Potentially toxic and tissue accumulated elements, such as cadmium, cobalt, mercury, and lead, are present only in trace amounts and have no statistically significant differences with the comparison group, which precludes their potential toxic effects on the macroorganism. Ceramic-based CBCI may be effective and useful in medicine for restoration of the anatomic integrity and functions of the bone tissue.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Probing and exploiting the chaotic dynamics of a hydrodynamic photochemical oscillator to implement all the basic binary logic functions

The convective motions within a solution of a photochromic spiro-oxazine being irradiated by UV only on the bottom part of its volume, give rise to aperiodic spectrophotometric dynamics. In this paper, we study three nonlinear properties of the aperiodic time series: permutation entropy, short-term predictability and long-term unpredictability, and degree distribution of the visibility graph networks. After ascertaining the extracted chaotic features, we show how the aperiodic time series can be exploited to implement all the fundamental two-inputs binary logic functions (AND, OR, NAND, NOR, XOR, and XNOR) and some basic arithmetic operations (half-adder, full-adder, half-subtractor). This is possible due to the wide range of states a nonlinear system accesses in the course of its evolution. Therefore, the solution of the convective photochemical oscillator results in hardware for chaos-computing alternative to conventional complementary metal-oxide semiconductor-based integrated circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense matrix. This is most commonly iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. The overall computational cost is therefore formally cubic in the number of electrons or matrix size. To improve the numerical efficiency of this procedure, we propose a novel multiple rank delayed update scheme. This strategy enables probability evaluation with application of accepted moves to the matrices delayed until after a predetermined number of moves, K . The accepted events are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency via matrix-matrix operations instead of matrix-vector operations. Here this procedure does not change the underlying Monte Carlo sampling or its statistical efficiency. For calculations on large systems and algorithms such as diffusion Monte Carlo where the acceptance ratio is high, order of magnitude improvements in the update time can be obtained on both multi- core CPUs and GPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

DR-RINS: Digital real-time relative intensity noise suppressor for pump–probe spectroscopy and microscopy

Relative intensity noise (RIN) inherent in fiber lasers poses a serious obstacle to their use in pump–probe spectroscopy and imaging. RIN can be removed through an analog balanced detector, or, as we have previously shown, software adaptive noise cancellation (ANC) on digitized signals. One major drawback to software ANC is the added time required for digitizing and post-processing. In this article, we describe a design for ANC on a field-programmable gate array (FPGA), making use of high-level synthesis tools and fixed-point arithmetic to achieve real-time laser RIN suppression at 25 MHz sample rates. Unlike the software-ANC approach, the FPGA-ANC device can serve as a dedicated drop-in denoiser, placed between the detectors and a commercial lock-in amplifier. We demonstrate its application to transient absorption spectroscopy and microscopy, lowering the noise floor to ~17 dB above the shot noise limit. Furthermore, we demonstrate a dramatic improvement in data acquisition time from ~6 h to ~5 min in a real-time imaging scenario.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Impact of areal-density asymmetries on the loss of confinement and ignition threshold in inertial confinement fusion capsules

In inertial confinement fusion implosion experiments, variations in the shell areal density reduce the shell’s inertia to confine the core pressure. Distorted capsules with large areal-density modulations decompress faster than uniform capsules in the disassembly phase. A simple 3-D analytic hot-spot model is derived to include the effects of low-mode areal-density modulations in the ignition criterion. The generalized 3-D ignition criterion for low modes is shown to depend on both the harmonic mean and the arithmetic mean of the areal density. Here, the “thin spots” in the shell are shown to dominate the loss of confinement as reflected by the harmonic mean definition of areal densities.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Toward exascale whole-device modeling of fusion devices: Porting the GENE gyrokinetic microturbulence code to GPU

GENE solves the five-dimensional gyrokinetic equations to simulate the development and evolution of plasma microturbulence in magnetic fusion devices. The plasma model used is close to first principles and computationally very expensive to solve in the relevant physical regimes. In order to use the emerging computational capabilities to gain new physics insights, several new numerical and computational developments are required. Here, we focus on the fact that it is crucial to efficiently utilize GPUs (graphics processing units) that provide the vast majority of the computational power on such systems. In this paper, we describe the various porting approaches considered and given the constraints of the GENE code and its development model, justify the decisions made, and describe the path taken in porting GENE to GPUs. We introduce a novel library called gtensor that was developed along the way to support the process. Performance results are presented for the ported code, which in a single node of the Summit supercomputer achieves a speed-up of almost 15× compared to running on central processing unit (CPU) only. Typical GPU kernels are memory-bound, achieving about 90% of peak. Our analysis shows that there is still room for improvement if we can refactor/fuse kernels to achieve higher arithmetic intensity. We also performed a weak parallel scalability study, which shows that the code runs well on a massively parallel system, but communication costs start becoming a significant bottleneck.

Germaschewski, K. (ORCID:0000000284956354)↗