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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Exact representations of many-body interactions with restricted-Boltzmann-machine neural networks

Restricted Boltzmann machines (RBMs) are simple statistical models defined on a bipartite graph which have been successfully used in studying more complicated many-body systems, both classical and quantum. In this work, we exploit the representation power of RBMs to provide an exact decomposition of many-body contact interactions into one-body operators coupled to discrete auxiliary fields. This construction generalizes the well known Hirsch's transform used for the Hubbard model to more complicated theories such as pionless effective field theory in nuclear physics, which we analyze in detail. Finally, we also discuss possible applications of our mapping for quantum annealing applications and conclude with some implications for RBM parameter optimization through machine learning.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Two supramolecular complexes based on polyoxometalates and Co-EDTA units via covalent connection or non-covalent interaction

Two new 3D network organic-inorganic hybrid supramolecular complexes ([Na{sub 6}(CoEDTA){sub 2}(H{sub 2}O){sub 13}]·(H{sub 2}SiW{sub 12}O{sub 40})·xH{sub 2}O)n (1) and [CoH{sub 4}EDTA(H{sub 2}O)]{sub 2}(SiW{sub 12}O{sub 40})·15H{sub 2}O (2) (H{sub 4}EDTA=Ethylenediamine tetraacetic acid) have been successfully synthesized by solution method, and characterized by infrared spectrum (IR), thermogravimetric-differential thermal analysis (TG-DTA), cyclic voltammetry (CV) and single{sup −}crystal X-ray diffraction (XRD). Both of the complexes are the supramolecules, but with different liking mode, they are two representative models of supramolecule. complex (1) is a 3D infinite network supramolecular coordination polymer with a rare multi-metal sturcture of sodium-cobalt-containing, which is mainly linked through coordinate-covalent bonds. While complex (2) is normal supramolecule, which linked by non-covalent interactions, such as H-bonding interaction, electrostatic interaction and van der waals force. Both of complex (1) and (2) exhibit good catalytic activities for catalytic oxidation of methanol, when the initial concentration of methanol is 3.0 g m{sup −3}, flow rate is 10 mL min{sup −1}, and the quality of catalyst is 0.2 g, for complex (1) and complex (2) the maximum elimination rates of methanol are 85% (150 °C) and 92% (120 °C), respectively. - Graphical abstract: Two new organic-inorganic hybrid supramolecular complexes based on Co-EDTA, and Keggin polyanions have been successfully synthesized with different pH value by solution method. They are attributed to two representative models of supramolecule. Complex(1) is an infinite coordination polymer with a rare multi-metal sturcture of sodium-cobalt-containing, which is mainly linked through covalent bonds. Complex (2) is a normal supramolecule, which linked by non-covalent interactions of H-bonding interaction, electrostatic interaction and van der waals force. - Highlights: • Two supramolecules are linked by covalent or non-covalent interactions. • They are attributed to two representative models of supramolecule. • A rare multi-metal infinite supramolecular coordination polymer was formed. • They exhibit good catalytic activities for catalytic oxidation of methanol.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. Lastly, we comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Exact block encoding of imaginary time evolution with universal quantum neural networks

We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the nonunitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave-function is closed under the action of the imaginary time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the yields the ground state of the system. The number of qubits grows linearly with the number of interactions and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via midcircuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and number of interactions, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the number of interactions, and circuit depth grows linearly with imaginary time only. Every midcircuit measurement has a postselection success probability, and the overall success probability is equal to the product of the probabilities of the midcircuit measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

Abstract The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å −1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

Science & Technology - Other Topics↗
DOE OSTI2026

Simultaneous global and local clustering in multiplex networks with covariate information

Understanding both global and layer-specific group structures is useful for uncovering complex patterns in networks with multiple interaction types. In this work, we introduce a new model, the hierarchical multiplex stochastic blockmodel, which simultaneously detects communities within individual layers of a multiplex network while inferring a global node clustering across the layers. A stochastic blockmodel is assumed in each layer, with probabilities of layer-level group memberships determined by a node’s global group assignment. Our model uses a Bayesian framework, employing a probit stick-breaking process to construct node-specific mixing proportions over a set of shared Griffiths–Engen–McCloseky distributions. These proportions determine layer-level community assignment, allowing for an unknown and varying number of groups across layers, while incorporating nodal covariate information to inform the global clustering. We propose a scalable variational inference procedure with parallelisable updates for application to large networks. Extensive simulation studies demonstrate our model’s ability to accurately recover both global and layer-level clusters in complicated settings, and applications to real data showcase the model’s effectiveness in uncovering interesting latent network structure.

community detection↗
NASA NTRS2018

NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment

In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program.

Neural↗
DOE OSTI2017

Systems Level Dissection of Anaerobic Methane Cycling: Quantitative Measurements of Single Cell Ecophysiology, Genetic Mechanisms, and Microbial Interactions

The global biological CH4 cycle is largely controlled through coordinated and often intimate microbial interactions between archaea and bacteria, the majority of which are still unknown or have been only cursorily identified. Members of the methanotrophic archaea, aka ‘ANME’, are believed to play a major role in the cycling of methane in anoxic environments coupled to sulfate, nitrate, and possibly iron and manganese oxides, frequently forming diverse physical and metabolic partnerships with a range of bacteria. The thermodynamic challenges overcome by the ANME and their bacterial partners and corresponding slow rates of growth are common characteristics in anaerobic ecosystems, and, in stark contrast to most cultured microorganisms, this type of energy and resource limited microbial lifestyle is likely the norm in the environment. While we have gained an in-depth systems level understanding of fast-growing, energy-replete microorganisms, comparatively little is known about the dynamics of cell respiration, growth, protein turnover, gene expression, and energy storage in the slow-growing microbial majority. These fundamental properties, combined with the observed metabolic and symbiotic versatility of methanotrophic ANME, make these cooperative microbial systems a relevant (albeit challenging) system to study and for which to develop and optimize culture-independent methodologies, which enable a systems-level understanding of microbial interactions and metabolic networks. We used an integrative systems biology approach to study anaerobic sediment microcosms and methane-oxidizing bioreactors and expanded our understanding of the methanotrophic ANME archaea, their interactions with physically-associated bacteria, ecophysiological characteristics, and underlying genetic basis for cooperative microbial methane-oxidation linked with different terminal electron acceptors. Our approach is inherently multi-disciplinary and multi-scaled, combining transcriptional and proteomic analyses with high resolution microscopy techniques, and stable isotopic and chemical analyses that span community level ‘omics investigations (cm scale) to interspecies consortia (µm scale), to the individual cell and its subcellular components (nm scale). We have organized our methodological approach into three broad categories, RNA-based, Protein-targeted and Geochemical, each encompassing a range of scales, with many techniques and resulting datasets that are highly complementary with one another, and together, offer a unique systems-level perspective of methane-based microbial interactions.

03 NATURAL GAS↗
NASA NTRS2000

Implementing Journaling in a Linux Shared Disk File System

In computer systems today, speed and responsiveness is often determined by network and storage subsystem performance. Faster, more scalable networking interfaces like Fibre Channel and Gigabit Ethernet provide the scaffolding from which higher performance computer systems implementations may be constructed, but new thinking is required about how machines interact with network-enabled storage devices. In this paper we describe how we implemented journaling in the Global File System (GFS), a shared-disk, cluster file system for Linux. Our previous three papers on GFS at the Mass Storage Symposium discussed our first three GFS implementations, their performance, and the lessons learned. Our fourth paper describes, appropriately enough, the evolution of GFS version 3 to version 4, which supports journaling and recovery from client failures. In addition, GFS scalability tests extending to 8 machines accessing 8 4-disk enclosures were conducted: these tests showed good scaling. We describe the GFS cluster infrastructure, which is necessary for proper recovery from machine and disk failures in a collection of machines sharing disks using GFS. Finally, we discuss the suitability of Linux for handling the big data requirements of supercomputing centers.

Preslan, Kenneth W.↗
DOE OSTI2014

Two three-dimensional coordination polymers of lead(II) with iminodiacetate and naphthalene-dicarboxylate anions: Synthesis, characterization and luminescence behavior

Two lead(II) compounds [Pb{sub 3}(idiac){sub 3}(phen){sub 2}(H{sub 2}O)]·2(H{sub 2}O) (1) and [Pb(ndc)]{sub n} (2), where H{sub 2}idiac=iminodiacetic acid, phen=1,10-phenanthroline and H{sub 2}ndc=naphthalene-2,6-dicarboxylic acid, have been synthesized and structurally characterized. Single crystal X-ray diffraction analysis showed that compound 1 is a discrete trinuclear complex (of two-fold symmetry) which evolves to a supramolecular 3D network via π–π interactions, while in compound 2 the naphthalene dicarboxylate anion act as a linker to form a three dimensional architecture, where the anion adopts a bis-(bidentate bridging) coordination mode connecting four Pb(II) centers. The photoluminescence property of the two complexes has been studied. - graphical abstract: Two new topologically different 1D coordination polymers formed by Pb{sub 4} clusters have been synthesized and characterized by x-ray analysis. The luminescence and thermal properties have been studied. - Highlights: • 1 is a trinuclear complex of Pb(II) growing to 3D network via weak interactions. • In 1, layers of (4,4) rhomboidal topology are identified. • In 2, the ndc anion adopts interesting bis-(bidentate bridging) coordination. • In 2, network is reinforced by C–H…π-ring interactions between the ndc rings.

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

A Gaussian graphical model approach to climate networks

Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Next Generation Protein Interactomes for Plant Systems Biology and Biomass Feedstock Research

Biofuel crop cultivation is a necessary step in heading towards a sustainable future, making their genomic studies a priority. While technology platforms that currently exist for studying non-model crop species, like switch-grass or sorghum, have yielded large quantities of genomic and expression data, still a large gap exists between molecular mechanism and phenotype. The aspect of molecular activity at the level of protein-protein interactions has recently begun to bridge this gap, providing a more global perspective. Interactome analysis has defined more specific functional roles of proteins based on their interaction partners, neighborhoods, and other network features, making it possible to distinguish unique modules of immune response to different plant pathogens(Jiang, Dong, and Zhang 2016). As we work towards cultivating heartier biofuel crops, interactome data will lead to uncovering crop-specific defense and development networks. However, the collection of protein interaction data has been limited to expensive, time-consuming, hard-to-scale assays that mostly require cloned ORF collections. For these reasons, we have successfully developed a highly scalable, economical, and sensitive yeast two-hybrid assay, ProCREate, that can be universally applied to generate proteome-wide primary interactome data. ProCREate enables en masse pooling and massively paralleled sequencing for the identification of interacting proteins by exploiting Cre-lox recombination. ProCREate can be used to screen ORF/cDNA libraries from feedstock plant tissues. The interactome data generated will yield deeper insight into many molecular processes and pathways that can be used to guide improvement of feedstock productivity and sustainability.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1995

Reducing neural network training time with parallel processing

Obtaining optimal solutions for engineering design problems is often expensive because the process typically requires numerous iterations involving analysis and optimization programs. Previous research has shown that a near optimum solution can be obtained in less time by simulating a slow, expensive analysis with a fast, inexpensive neural network. A new approach has been developed to further reduce this time. This approach decomposes a large neural network into many smaller neural networks that can be trained in parallel. Guidelines are developed to avoid some of the pitfalls when training smaller neural networks in parallel. These guidelines allow the engineer: to determine the number of nodes on the hidden layer of the smaller neural networks; to choose the initial training weights; and to select a network configuration that will capture the interactions among the smaller neural networks. This paper presents results describing how these guidelines are developed.

Rogers, James L., Jr.↗
NASA NTRS2007

XNsim: Internet-Enabled Collaborative Distributed Simulation via an Extensible Network

In this paper, the XNsim approach to achieve Internet-enabled, dynamically scalable collaborative distributed simulation capabilities is presented. With this approach, a complete simulation can be assembled from shared component subsystems written in different formats, that run on different computing platforms, with different sampling rates, in different geographic locations, and over singlelmultiple networks. The subsystems interact securely with each other via the Internet. Furthermore, the simulation topology can be dynamically modified. The distributed simulation uses a combination of hub-and-spoke and peer-topeer network topology. A proof-of-concept demonstrator is also presented. The XNsim demonstrator can be accessed at http://www.jsc.draver.corn/xn that hosts various examples of Internet enabled simulations.

Novotny, John↗
DOE OSTI2014

Integrated network design and scheduling problems :

We consider the class of integrated network design and scheduling problems. These problems focus on selecting and scheduling operations that will change the characteristics of a network, while being speci cally concerned with the performance of the network over time. Motivating applications of INDS problems include infrastructure restoration after extreme events and building humanitarian distribution supply chains. While similar models have been proposed, no one has performed an extensive review of INDS problems from their complexity, network and scheduling characteristics, information, and solution methods. We examine INDS problems under a parallel identical machine scheduling environment where the performance of the network is evaluated by solving classic network optimization problems. We classify that all considered INDS problems as NP-Hard and propose a novel heuristic dispatching rule algorithm that selects and schedules sets of arcs based on their interactions in the network. We present computational analysis based on realistic data sets representing the infrastructures of coastal New Hanover County, North Carolina, lower Manhattan, New York, and a realistic arti cial community CLARC County. These tests demonstrate the importance of a dispatching rule to arrive at near-optimal solutions during real-time decision making activities. We extend INDS problems to incorporate release dates which represent the earliest an operation can be performed and exible release dates through the introduction of specialized machine(s) that can perform work to move the release date earlier in time. An online optimization setting is explored where the release date of a component is not known.

Nurre, Sarah G.↗
DOE OSTI2021

Automated Cyber Security Testing Platform for Industrial Control Systems

Nuclear Power Plants (NPPs) are a complex system of coupled physics controlled by a network of Programmable Logic Controllers (PLCs). These PLCs communicate process data across the network to coordinate control actions with each other and inform the operators of process variables and control decisions. Networking the PLCs allows more effective process control and provides the operator more information which results in more efficient plant operation. This interconnectivity creates new security issues, as operators have more access to the plant controls, so will bad actors. As plant networks become more digitized and encompass more sophisticated controllers, the network surface exposed to cyber interference grows. Understanding the dynamics of these coupled systems of physics, control logic, and network communications is critical to their protection. The research into the cybersecurity of the Operational Technologies of NPPs is developing and requires a platform that can allow high fidelity physics simulations to interact with digital networks of controllers. This will require three main components: a network simulation environment, a physics simulator, and virtual PLCs (vPLC) that represent typical industry hardware. A platform that incorporates these three components to provide the most accurate representation of actual NPP networks and controllers is developed in this paper.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Ab Initio Dynamics of Graphene and Graphyne Electrodes in Vacuum and in the Presence of Electrolytes

Graphene and graphyne electrodes are both relevant for electrochemical energy storage applications due to their unique physical, chemical, and electronic properties. Graphyne has been considered even more attractive than graphene due to its larger specific surface area, greater electronic mobility, and intrinsic band gap. The analyses presented in this work reveal relevant features of graphyne in an interaction with an electrolyte that can be useful for practical applications. Although there is already a considerable volume of work on graphyne, the analysis of its properties taking into account the dynamics of its network and the interaction with the condensed medium has not yet been presented. Here, ab initio molecular dynamics simulations were performed to study the energetic, structural, spectroscopic, and electronic properties of graphene and graphyne electrodes in vacuum and in the presence of an electrolyte (EMIM-BF 4 ). A detailed analysis of the electrode–electrolyte interaction is reported for both systems. Structural features are presented in terms of both radial and spatial distribution functions and angular orientation distributions. Power spectral analyses and molecular orbital maps provided important insights into the intermolecular interactions in graphene and graphyne systems, describing the main differences as well as revealing similarities between them.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗