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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Understanding attosecond streaking

Abstract This tutorial provides an overview on the theory of attosecond streaking, a pump-probe scheme to extract timing information of ionization processes that has been widely used in the past decade. Emphasis is put on the origin of the Coulomb-laser-coupling (CLC) term, which is crucial in the interpretation of streaking delays. Having gained a proper understanding of how the CLC terms in various publications relate to each other, we will be able to analyze in which regime the streaking delay can be split into a measurement-induced CLC term and a ‘pure’ ionization delay and under which conditions this splitting may break down. Thus we address the long-standing question of the validity of the widely applied interpretation of the streaking delay as a sum of the CLC term and a ‘pure’ ionization delay.

Physics↗
DOE OSTI2023

New value of old knowledge: sulphur-based GaAs surface passivation and potential GaAs application in molecular electronics and spintronics

GaAs is well known for its extremely high electron mobility and direct band gap. Owing to the technological advances in silicon-based technology, GaAs has been limited to niche areas. This paper discusses the application of GaAs in molecular electronics and spintronics as a potential field for considering this amazing but challenging material. GaAs is challenging because its surface is characterized by a high density of surface states, which precludes the utilization of this semiconducting material in mainstream devices. Sulfur(S)-based passivation has been found to be significantly useful for reducing the effect of dangling bonds and was researched thoroughly. GaAs applications in molecular spintronics and electronics can benefit significantly from prior knowledge of GaAs and S interactions because S is a popular functional group for bonding molecular device elements with different semiconductors and metals. In this article, the problem associated with the GaAs surface is discussed in a tutorial form. A wide variety of surface passivation methods has been briefly introduced. We attempted to highlight the significant differences in the S-GaAs interactions for different S passivation methods. We also elaborate on the mechanisms and atomic-scale understanding of the variation in surface chemistry and reconstruction due to various S passivation methods. It is envisioned that GaAs and thiol-terminated molecule-based novel devices can exhibit innovative device characteristics and bring the added advantage of S-based passivation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.

Physics↗
DOE OSTI2025

Quantum simulation of boson-related Hamiltonians: techniques, effective Hamiltonian construction, and error analysis

Elementary quantum mechanics proposes that a closed physical system consistently evolves in a reversible manner. However, control and readout necessitate the coupling of the quantum system to the external environment, subjecting it to relaxation and decoherence. Consequently, system-environment interactions are indispensable for simulating physically significant theories. A broad spectrum of physical systems in condensed-matter and high-energy physics, vibrational spectroscopy, and circuit and cavity QED necessitates the incorporation of bosonic degrees of freedom, such as phonons, photons, and gluons, into optimized fermion algorithms for near-future quantum simulations. In particular, when a quantum system is surrounded by an external environment, its basic physics can usually be simplified to a spin or fermionic system interacting with bosonic modes. Nevertheless, troublesome factors such as the magnitude of the bosonic degrees of freedom typically complicate the direct quantum simulation of these interacting models, necessitating the consideration of a comprehensive plan. This strategy should specifically include a suitable fermion/boson-to-qubit mapping scheme to encode sufficiently large yet manageable bosonic modes, and a method for truncating and/or downfolding the Hamiltonian to the defined subspace for performing an approximate but highly accurate simulation, guided by rigorous error analysis. In this pedagogical tutorial review, we aim to provide such an exhaustive strategy, focusing on encoding and simulating certain bosonic-related model Hamiltonians, inclusive of their static properties and time evolutions. Specifically, we emphasize two aspects: (1) the discussion of recently developed quantum algorithms for these interacting models and the construction of effective Hamiltonians, and (2) a detailed analysis regarding a tightened error bound for truncating the bosonic modes for a class of fermion-boson interacting Hamiltonians.

bosonic Hamiltonian↗
DOE OSTI2021

Statistically-informed deep learning for gravitational wave parameter estimation

We introduce deep learning models to estimate the masses of the binary components of black hole mergers, $(m_1,m_2)$, and three astrophysical properties of the post-merger compact remnant, namely, the final spin, $a_\mathrm f$, and the frequency and damping time of the ringdown oscillations of the fundamental $\ell = m = 2$ bar mode, $(\omega_\mathrm R, \omega_\mathrm I)$. Our neural networks combine a modified WaveNet architecture with contrastive learning and normalizing flow. We validate these models against a Gaussian conjugate prior family whose posterior distribution is described by a closed analytical expression. Upon confirming that our models produce statistically consistent results, we used them to estimate the astrophysical parameters $(m_1,m_2, a_\mathrm f, \omega_\mathrm R, \omega_\mathrm I)$ of five binary black holes: GW150914, GW170104, GW170814, GW190521 and GW190630. We use PyCBC Inference to directly compare traditional Bayesian methodologies for parameter estimation with our deep learning based posterior distributions. Our results show that our neural network models predict posterior distributions that encode physical correlations, and that our data-driven median results and 90% confidence intervals are similar to those produced with gravitational wave Bayesian analyses. This methodology requires a single V100 NVIDIA GPU to produce median values and posterior distributions within two milliseconds for each event. Furthermore, this neural network, and a tutorial for its use, are available at the Data and Learning Hub for Science.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Applications of physics informed neural operators

Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code , an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

3D Optical Coherence Tomography image processing in BISCAP: characterization of biofilm structure and properties

Abstract Motivation BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP’s first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Results Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. Availability and implementation BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d.

Narciso, Diogo A. C. (ORCID:0000000227767477)↗
DOE OSTI2019

Multi-scale structural analysis of proteins by deep semantic segmentation

Abstract Motivation Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation—a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment. Results We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design. Availability and implementation The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information. Supplementary information Supplementary data are available at Bioinformatics online.

Eguchi, Raphael R.↗
DOE OSTI2021

A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. Further, we provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The simulated catalogue of optical transients and correlated hosts (SCOTCH)

ABSTRACT As we observe a rapidly growing number of astrophysical transients, we learn more about the diverse host galaxy environments in which they occur. Host galaxy information can be used to purify samples of cosmological Type Ia supernovae, uncover the progenitor systems of individual classes, and facilitate low-latency follow-up of rare and peculiar explosions. In this work, we develop a novel data-driven methodology to simulate the time-domain sky that includes detailed modelling of the probability density function for multiple transient classes conditioned on host galaxy magnitudes, colours, star formation rates, and masses. We have designed these simulations to optimize photometric classification and analysis in upcoming large synoptic surveys. We integrate host galaxy information into the snana simulation framework to construct the simulated catalogue of optical transients and correlated hosts (SCOTCH, a publicly available catalogue of 5-million idealized transient light curves in LSST passbands and their host galaxy properties over the redshift range 0 < z < 3. This catalogue includes supernovae, tidal disruption events, kilonovae, and active galactic nuclei. Each light curve consists of true top-of-the-galaxy magnitudes sampled with high (≲2 d) cadence. In conjunction with SCOTCH, we also release an associated set of tutorials and transient-specific libraries to enable simulations of arbitrary space- and ground-based surveys. Our methodology is being used to test critical science infrastructure in advance of surveys by the Vera C. Rubin Observatory and the Nancy G. Roman Space Telescope.

Astronomy & Astrophysics↗
DOE OSTI2023

Quantitative elemental imaging in eukaryotic algae

Abstract All organisms, fundamentally, are made from the same raw material, namely the elements of the periodic table. Biochemical diversity is achieved by how these elements are utilized, for what purpose, and in which physical location. Determining elemental distributions, especially those of trace elements that facilitate metabolism as cofactors in the active centers of essential enzymes, can determine the state of metabolism, the nutritional status, or the developmental stage of an organism. Photosynthetic eukaryotes, especially algae, are excellent subjects for quantitative analysis of elemental distribution. These microbes utilize unique metabolic pathways that require various trace nutrients at their core to enable their operation. Photosynthetic microbes also have important environmental roles as primary producers in habitats with limited nutrient supplies or toxin contaminations. Accordingly, photosynthetic eukaryotes are of great interest for biotechnological exploitation, carbon sequestration, and bioremediation, with many of the applications involving various trace elements and consequently affecting their quota and intracellular distribution. A number of diverse applications were developed for elemental imaging, allowing subcellular resolution, with X-ray fluorescence microscopy (XFM, XRF) being at the forefront, enabling quantitative descriptions of intact cells in a non-destructive method. This Tutorial Review summarizes the workflow of a quantitative, single-cell elemental distribution analysis of a eukaryotic alga using XFM.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

FUSARIUM-ID v.3.0: An Updated, Downloadable Resource for Fusarium Species Identification

Species within Fusarium are of global agricultural, medical, and food/feed safety concern and have been extensively characterized. However, accurate identification of species is challenging and usually requires DNA sequence data. FUSARIUM-ID ( http://isolate.fusariumdb.org/blast.php ) is a publicly available database designed to support the identification of Fusarium species using sequences of multiple phylogenetically informative loci, especially the highly informative ~680-bp 5' portion of the translation elongation factor 1-alpha (TEF1) gene that has been adopted as the primary barcoding locus in the genus. However, FUSARIUM-ID v.1.0 and 2.0 had several limitations, including inconsistent metadata annotation for the archived sequences and poor representation of some species complexes and marker loci. Here, we present FUSARIUM-ID v.3.0, which provides the following improvements: (i) additional and updated annotation of metadata for isolates associated with each sequence, (ii) expanded taxon representation in the TEF1 sequence database, (iii) availability of the sequence database as a downloadable file to enable local BLAST queries, and (iv) a tutorial file for users to perform local BLAST searches using either freely available software, such as SequenceServer, BLAST+ executable in the command line, and Galaxy, or the proprietary Geneious software. FUSARIUM-ID will be updated on a regular basis by archiving sequences of TEF1 and other loci from newly identified species and greater in-depth sampling of currently recognized species.

Plant Sciences↗
DOE OSTI2018

Setup and Characterization of a 137 CS Dosimetry Calibration Source in a Space-constrained Environment

Proper characterization of 137 Cs sources used for dosimeter calibration and performance testing is crucial for accurate and precise knowledge of air kerma rate and delivered dose. A 269 GBq 137 Cs source was relocated to a new facility, which had a footprint of approximately 2.8 m × 3.6 m and a 3.4 m high ceiling. A small room size, such as in the new facility, may significantly increase backscatter from the walls or room return. Due to the limited source strength, a relatively close irradiation position of 1.00 m from the source was selected to decrease required exposure times. Proximity to the small but cylindrical source has the potential to alter the 1/r 2 relationship (inverse square law) of air kerma rate with distance associated with point sources. Practical tutorials concerning dosimetry irradiation facilities are largely absent from the archival literature. For those reasons, standard characterization experiments were repeated multiple times, great care was taken with positioning, several experiments were added to the standard ones, an ion chamber and dosimeters were used rather than film for greater accuracy and precision, and practical details of the process were recorded. The impacts of room size and finite source dimensions were quantified and mitigated by the efforts reported here. Finally, recommendations for simple but thorough facility characterization, beam calibration, and quality control resulted.

42 ENGINEERING↗
DOE OSTI2020

A method for achieving complete microbial genomes and improving bins from metagenomics data

Metagenomics facilitates the study of the genetic information from uncultured microbes and complex microbial communities. Assembling complete microbial genomes ( i.e ., circular with no misassemblies) from metagenomics data is difficult because most samples have high organismal complexity and strain diversity. Less than 100 circularized bacterial and archaeal genomes have been assembled from metagenomics data despite the thousands of datasets that are available. Circularized genomes are important for (1) building a reference collection as scaffolds for future assemblies, (2) providing complete gene content of a genome, (3) confirming little or no contamination of a genome, (4) studying the genomic context and synteny of genes, and (5) linking protein coding genes to ribosomal RNA genes to aid metabolic inference in 16S rRNA gene sequencing studies. We developed a method to achieve circularized genomes using iterative assembly, binning, and read mapping. In addition, this method exposes potential misassemblies from k-mer based assemblies. We chose species of the Candidate Phyla Radiation (CPR) to focus our initial efforts because they have small genomes and are only known to have one ribosomal RNA operon. We present 34 circular CPR genomes, one circular Margulisbacteria genome, and two circular megaphage genomes from 19 public and published datasets. We demonstrate findings that would likely be difficult without circularizing genomes, including that ribosomal genes are likely not operonic in the majority of CPR, and that some CPR harbor diverged forms of RNase P RNA. Code and a tutorial for this method is available at https://github.com/lmlui/Jorg .

Lui, Lauren↗
DOE OSTI2026

Hybrid Oscillator-Qubit Quantum Processors: Instruction Set Architectures, Abstract Machine Models, and Applications

This tutorial offers a pedagogical guide to hybrid quantum processors that integrate discrete-variable (DV) qubits and continuous-variable (CV) oscillators. Aimed at computer scientists, engineers, and physicists, it provides an overview of the experimental, algorithmic, and architectural aspects of this novel and rapidly developing hardware model. Experimental realizations of this model include superconducting, trapped-ion, and neutral-atom platforms. By combining DV and CV components, hybrid oscillator-qubit processors enable a powerful new paradigm that offers complementary strengths for quantum control, error correction, computation, and simulation. Working toward the goal of a full-stack system connecting applications to CV-DV hardware, we define and formulate abstract machine models and instruction set architectures. These essential abstractions enable codesign of hardware and software, and resource estimation for exploring the potential of current and future hardware for computational and simulation tasks. Using these abstractions, we present both new and existing examples that illustrate the benefits of hybrid CV-DV processors relative to traditional DV-only hardware in computation as well as quantum simulation of physical models. Examples include algorithms for transferring states between DV and CV systems, performing the quantum Fourier transform, and simulation of lattice gauge theories. Relative to qubit-only hardware, the bosonic degrees of freedom natively available in hybrid architectures can substantially reduce the circuit complexity of simulations for physical models containing bosons. A key technique is the extension of quantum signal processing ideas to CV-DV systems. This work is intended to serve as a timely and comprehensive guide to this relatively unexplored yet promising approach to quantum computation and to provide a road map to guide future development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗