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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Multilayer Network Clarifies Prevailing Water Consumption Telecouplings in the United States

Virtual water flows are used to map the indirect water consumption connections implied by the supply chain of a city, region, or country. This information can be used to manage supply chains to achieve environmental policy objectives and mitigate environmental risks to critical supply chains. A limitation of prior work is that these flows are typically analyzed using monolayer networks, which ignores crucial intersectoral or interlayer couplings. Here, in this study, we use a multilayer network to account for such couplings when analyzing blue virtual water flows in the United States. Our multilayer network consists of 115 different regions (nodes), covering the entire conterminous United States; 41 coupled economic sectors (layers); and ∼2 × 10 7 possible links. To analyze the multilayer network, we focus on three fundamental network properties: topological connectivity, mesoscale structure, and node centrality. The network has a high connectivity, with each node being on average connected to roughly 2/3 of the network's nodes. Interlayer flows are a major driver of connectivity, representing ∼54% of all the network's connections. Five different groups of tightly connected nodes (communities) characterize the network. Each community represents a preferred spatial mode of long-range virtual water interaction within the United States. We find that large (populous) cities have a stronger influence than small ones on network functioning because they attract and recirculate more virtual water through their supply chains. Our results also highlight differences between the multilayer and monolayer virtual water network, which overall show that the former provides a more realistic representation of virtual water flows.

Garcia Gonzalez, Susana↗
DOE OSTI2025

Noisy quantum trees: infinite protection without correction

We study quantum networks with tree structures, in which information propagates from a root to leaves. At each node in the network, the received qubit unitarily interacts with fresh ancilla qubits, after which each qubit is sent through a noisy channel to a different node in the next level. Therefore, as the tree depth grows, there is a competition between the irreversible effect of noise and the protection against such noise achieved by the delocalization of information. In the classical setting, where each node simply copies the input bit into multiple output bits, this model has been studied as the broadcasting or reconstruction problem on trees, which has broad applications. In this work, we study the quantum version of this problem. We consider a Clifford encoder at each node that encodes the input qubit in a stabilizer code, along with a single qubit Pauli noise channel at each edge. Such noisy quantum trees describe a scenario in which one has access to a stream of fresh (low-entropy) ancilla qubits, but cannot perform error correction. Therefore, they provide a different perspective on quantum fault tolerance. Furthermore, they provide a useful model for describing the effect of noise within the encoders of concatenated codes. We prove that above certain noise thresholds, which depend on the properties of the code such as its distance, as well as the properties of the encoder, information decays exponentially with the depth of the tree. On the other hand, by studying certain efficient decoders, we prove that for codes with distance d ≥ 2 and for sufficiently small (but non-zero) noise, classical information and entanglement propagate over a noisy tree with infinite depth. Indeed, we find that this remains true even for binary trees with certain 2-qubit encoders at each node, which encodes the received qubit in the binary repetition code with distance d = 1.

Quantum information↗
DOE OSTI2020

Studies of surface adsorbate electronic structure and femtochemistry at the fundamental length and time scales. Final report

The electronic structure and ultrafast (10-15 s-femtosecond timescale) electron dynamics were investigated for clean and atom/molecule covered metal surfaces. The studies were performed by scanning tunneling microscopy (STM) to measure the structure of adsorbed atoms and molecules on metal surfaces, and to investigate their electronic properties. The electronic structure of the observed molecular networks was calculated by electronic structure theory in collaboration with Prof. Jin Zhao, who is a long-time collaborator, a Professor at the University of Science and Technology of China, and holds an Adjunct Professorship at the University of Pittsburgh. We also investigated the electronic properties of C60 molecules when they are templated by corrugated black phosphorous surfaces. We found unexpected charge delocalization that is enabled by the templating. This research was done in collaboration with Professor Min Feng at the Wuhan University, and who also holds an Adjunct Professorship at the University of Pittsburgh. Moreover, the electronic structure and electron dynamics in metal surfaces were investigated by time-resolved photoemission electron spectroscopy. The focus of ultrafast spectroscopy has been on the plasmonic response of silver surfaces. One direction has been to develop multidimensional (energy, momentum, and time) photoelectron spectroscopy of the coherent response of solid surfaces. This method was applied to study the collective electron excitations known generally as plasmons, which screen optical fields from penetration into metals. Although this collective response has been known for more than 60 years and is used extensively to deposit optical energy into metals, how this happens is poorly known. We investigated the plasmonic response of silver at the point where the dielectric response passes through zero and bulk plasmon is excited by light. We discovered that the plasmon excitation decays by exciting electrons from the Fermi level of a metal, which is contrary to what is believed in the plasmonic science community. This research has been performed in collaboration with Dr. Marcel Reutzel, who was a postdoctoral fellow working on this research at the University of Pittsburgh, and now has a faculty position at the University of Göttingen in Germany. Prof. Branko Gumhalter from the Institute of Physics in Zagreb contributed on the theory of plasmonic decay processes. Furthermore, we investigated the Floquet engineering of electronic bands in metals leading to multiphoton photoemission and above threshold photoemission. Finally, we demonstrated that it is possible to change the electronic structure of metals by application of optical fields. Our studies indicated that this happens on subfemtosecond time scale and could potentially be used in ultrafast information processing and quantum computation. Related document information

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Local structural investigation of non-crystalline materials at high pressure: the case of GeO 2 glass

Abstract Local structures play a crucial role in the structural polyamorphism and novel electronic properties of amorphous materials, but their accurate measurement at high pressure remains a formidable challenge. In this article, we use the local structure of network-forming GeO 2 glass as an example, to present our recent approaches and advances in high-energy x-ray diffraction, high-pressure x-ray absorption fine structure, and ab initio first-principles density functional theory calculations and simulations. Although GeO 2 glass is one of the best studied materials in the field of high pressure research due to its importance in glass theory and geophysical significance, there are still some long-standing puzzles, such as the existence of appreciable distinct fivefold [5] Ge coordination at low pressure and the sixfold-plus [6+] Ge coordination at ultrahigh pressure. Our work sheds light on the origin of pressure-induced polyamorphism of GeO 2 glass, and the [5] Ge polyhedral units may be the dominant species in the densification mechanism of network-forming glasses from tetrahedral to octahedral amorphous structures.

Physics↗
DOE OSTI2026

PowerMappeR: Power-Optimized Mapping of SNNs onto ReRAM Crossbars coupled via Packet-Switched NoCs

Many recent efforts in developing hardware-accelerated spiking neural networks (SNNs) are characterized by deep co-design between algorithms, architectures, and devices. Architectural advances overcome device constraints by coupling together many small resistive-RAM (ReRAM) crossbars via a network-on-chip (NoC) for neuromorphic component operation. Concurrently, improved SNN training methods increase accuracy and structural sparsity in networks despite growing problem sizes. Finally, compilers leverage these attributes to minimize area and inter-crossbar communication while mapping large SNNs to sophisticated architectures. However, for compiler-driven co-design to realize increasingly complex and profitable optimizations, a compile-time view of power consumption is critical. We present PowerMappeR to express and optimize over mapping-, architecture-, and device-specific power consumption information. By modeling the dynamic power of well-established components, we develop an integer linear programming (ILP)-based, encoding-agnostic, parametric power estimation model. Using this model, we demonstrate practical improvements in area and inter-crossbar communication by 0%–9.5% and 1.4%–5.1%, respectively. We also limit hotspot formation during optimization, achieving comparable or better results in targeted metrics with up to 96.4%–97.1% restriction of hotspot magnitude. Finally, we introduce profile-guided formulations to reduce worst-case and expected-case hotspot magnitude by 40.7%–69.5% and 40.6%–56.3%, respectively. Optimizing worst-case hotspot magnitude incidentally improves expected-case magnitude by 10.85%–33.45%. Reciprocally, optimizing expected-case magnitude incidentally improves worst-case magnitude by 4.33%–39.87%. Validation against hardware simulators confirms that PowerMappeR can decrease dynamic power consumption by 12.6%–27.3%.

Pohl, Devin [ORNL] (ORCID:0009000040149027)↗
DOE OSTI2023

Emulation automation and model checking

A method of automating emulations is provided. The method comprising collecting publicly available network data over a predefined time interval, wherein the collected network data might comprise structured and unstructured data. Any unstructured data is converted into structured data. The original and converted structured data is stored in a database and compared to known network vulnerabilities. An emulated network is created according to the collected network data and the comparison of the structured data with known vulnerabilities. Virtual machines are created to run on the emulated network. Director programs and guest actor programs are run on the virtual machines, wherein the actor programs imitate real user behavior on the emulated network. The director programs deliver task commands to the guest actor programs to imitate real user behavior. The imitated behavior is presented to a user via an interface.

Urias, Vincent↗
DOE OSTI2021

Moving beyond the constraints of chemistry via crystal structure discovery with isotropic multiwell pair potentials

The rigid constraints of chemistry-dictated by quantum mechanics and the discrete nature of the atom-limit the set of observable atomic crystal structures. What structures are possible in the absence of these constraints? Here, we systematically crystallize one-component systems of particles interacting with isotropic multiwell pair potentials. We investigate two tunable families of pairwise interaction potentials. Our simulations self-assemble a multitude of crystal structures ranging from basic lattices to complex networks. Sixteen of the structures have natural analogs spanning all coordination numbers found in inorganic chemistry. Fifteen more are hitherto unknown and occupy the space between covalent and metallic coordination environments. The discovered crystal structures constitute targets for self-assembly and expand our understanding of what a crystal structure can look like.

crystal structures↗
DOE OSTI2026

Doping Metallic Grain Boundaries to Control Atomic Structure and Damage Tolerance

Grain boundaries often act as sites for crack and void nucleation during plastic deformation of metallic materials. While it is known that grain boundary character and structure can greatly influence this damage nucleation process, the current level of control over such details is limited. The objective of this project was to obtain a fundamental understanding of how metallic grain boundary structure can be controlled through intelligent doping, with the idea of inducing planned amorphous grain boundary phases or complexions. The effect of amorphous complexion structure on dislocation accommodation mechanisms was studied, to improve the field’s understanding of damage nucleation at a promising type of interface. Different microstructural descriptors were studied, as grain boundaries can have large variations and complexity of local structure, and influence the mechanical damage resistance of these features was tested. Nanocrystalline systems that contain amorphous grain boundary complexions were prioritized, as these have more extreme variations in interfacial structure and damage tolerance, with an emphasis on isolating the importance of complexion population (type, thickness, etc.), network topology, local structure, and local chemistry. This research used a combination of computational, experimental, and characterization techniques to isolate and understand the importance of nanoscale grain boundary structure and interfacial chemistry. Amorphous grain boundary complexions were found to clearly increase a material’s resistance to mechanical damage, with the local distribution of structural short-range order within the complexions found to be an important descriptor for damage and the proposed focus of future work in this area.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The hidden structure of hydrodynamic transport in random fracture networks

We study the large-scale dynamics and prediction of hydrodynamic transport in random fracture networks. The flow and transport behaviour is characterized by first passage times and displacement statistics, which show heavy tails and anomalous dispersion with a strong dependence on the injection condition. The origin of these behaviours is investigated in terms of Lagrangian velocities sampled equidistantly along particle trajectories, unlike classical sampling strategies at a constant rate. The velocity series are analysed by their copula density, the joint distribution of the velocity unit scores, which reveals a simple, albeit hidden, correlation structure that can be described by a Gaussian copula. Based on this insight, we derive a Langevin equation for the evolution of equidistant particle speeds. In this framework, particle motion is quantified by a stochastic time-domain random walk, the joint density of particle position, and speed satisfies a Klein–Kramers equation. The upscaled theory quantifies particle motion in terms of the characteristic fracture length scale and the distribution of Eulerian flow velocities. That is, it is predictive in the sense that it does not require the a priori knowledge of transport attributes. The upscaled model captures non-Fickian transport features, and their dependence on the injection conditions in terms of the velocity point statistics and average fracture length. It shows that the first passage times and displacement moments are dominated by extremes occurring at the first step. The presented approach integrates the interaction of flow and structure into a predictive model for large-scale transport in random fracture networks.

42 ENGINEERING↗
DOE OSTI2026

NN-OpInf

SAND2026-18878O The NN-OpInf tool is a PyTorch-based approach to operator inference that uses composable, structure-preserving neural networks to represent nonlinear operators. Operator inference is a machine learning method for inferring low-dimensional systems from data and polynomial models for system dynamics. However, many systems do not conform to polynomial structures, which NN-OpInf addresses by parameterizing operators with neural networks. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2021

Insight into the nanostructure of “water in salt” solutions: A SAXS/WAXS study on imide-based lithium salts aqueous solutions

"Water-in-salt " electrolyte (WISE) series have been studied for lithium-ion batteries and supercapacitor applications, but so far, most of the focus has been on the LiTFSI salt-based systems. Herein, we used small-angle X-ray scattering/wide-angle X-ray scattering (SAXS/WAXS) and molecular dynamics (MD) simulation to investigate the solvation structure of a series of lithium salts with four different symmetric anions: (bis(fluoro sulfonyl)imide (FSI), bis(trifluoromethane sulfonyl)imide (TFSI), bis(pentafluoroethane sulfonyl) imide (BETI) and bis(nonafluorobutane sulfonyl)imide (BNTI)), which have similar parent structures but different lengths of the fluorocarbon chains. Two competing structures were found in the four lithium salts aqueous solutions: anion solvated structure and anion network. The anion network plays a crucial role in obtaining a stable and enlarged electrochemical window. The d spacing of the anion solvated structure follows a linear correlation with the number of carbons in the fluorocarbon chains and an exponential correlation with the concentrations. Further, we also observed that the transition from one structure to another is predominantly controlled by the salt volume fraction for all imide-based aqueous solutions. This work provides a systematic study of the atomistic scale structures of a series of imide-based lithium salt aqueous solutions that could extend the knowledge of WIS electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2024

Generative $β$-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

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

Structure and Dynamics of CO 2 at the Air–Water Interface from Classical and Neural Network Potentials

The accurate description of the structure and dynamics of CO 2 at the instantaneous air–water interface, along with the effects of surface fluctuations on the CO 2 -transport processes, is essential for the development of negative emission technologies aimed at minimizing climate change. In this study, we performed molecular dynamics simulations of CO 2 at the air–water interface using neural network potentials (NNPs) trained on ab initio data generated through density-functional-theory-based molecular dynamics simulations. We compared these results with classical force fields to assess their performance in modeling interfacial CO 2 behavior. Our findings revealed that the asymmetric interactions, coupled with thermal surface fluctuations at the air–water interface, significantly influence CO 2 transport into the aqueous phase. The simulations demonstrate that classical force fields underestimate both the free energy of CO 2 transport and the strength of its interactions at the interface compared with the neural network potentials. In conclusion, the free energy and the interfacial dynamics of CO 2 are primarily influenced by the distribution of water within the instantaneous interfacial water layer, responsible for creating an asymmetric intermolecular interaction environment within the interfacial region.

Ab initio molecular dynamics↗
DOE OSTI2022

Spatiotemporal Graph Convolutional Networks for Earthquake Source Characterization

Abstract Accurate earthquake location and magnitude estimation play critical roles in seismology. Recent deep learning frameworks have produced encouraging results on various seismological tasks (e.g., earthquake detection, phase picking, seismic classification, and earthquake early warning). Many existing machine learning earthquake location methods utilize waveform information from a single station. However, multiple stations contain more complete information for earthquake source characterization. Inspired by recent successes in applying graph neural networks (GNNs) in graph‐structured data, we develop a Spatiotemporal Graph Neural Network (STGNN) for estimating earthquake locations and magnitudes. Our graph neural network leverages geographical and waveform information from multiple stations to construct graphs automatically and dynamically by adaptive message passing based on graphs' edges. Using a recent graph neural network and a fully convolutional neural network as baselines, we apply STGNN to earthquakes recorded by the Southern California Seismic Network from 2000 to 2019 and earthquakes collected in Oklahoma from 2014 to 2015. STGNN yields more accurate earthquake locations than those obtained by the baseline models and performs comparably in terms of depth and magnitude prediction, though the ability to predict depth and magnitude remains weak for all tested models. Our work demonstrates the potential of using GNNs and multiple stations for better automatic estimation of earthquake epicenters.

58 GEOSCIENCES↗
DOE OSTI2020

CSB-RNN: A Faster-Than-Realtime RNN Acceleration Framework with Compressed Structured Blocks

Recurrent Neural Networks (RNN) is widely applied to temporal sequence analysis, where real-time performance is usually in demand. However, RNN suffers a heavy computational workload as the model comes with a large weight matrix. To alleviate the pain, model compression (pruning) schemes have been proposed for RNN that pruning the redundant (near-zero) weight-values. On the one hand, the non-structured pruning methods achieve a considerable pruning rate while bringing the computational irregularity, which is un-friendly to parallel-hardware. On the other hand, the existing structured pruning methods consider the hardware parallelism; However, they suffer a poor pruning rate due to the restrict constraints on pruning structure. This paper presents CSB-RNN, an optimized full-stack RNN framework with the novel compressed structured block (CSB) technique. The CSB-pruned RNN model comes with both fine-granularity that benefits the pruning rate and regular structure that facilitates the hardware-parallelism. Further, we propose a novel hardware architecture for inferencing the CSB-pruned model. Different from conventional parallel hardware, this architecture solves the block-workload imbalance issue and achieves an over 95% hardware utilization. With the experiments on 10 RNN models in 5 application domains, the CSB-RNN realizes 7×-20× lossless compression and up to 50× acceptable lossy-compression, which is 2×-7× to the prior art. With the addition of the novel hardware, the compressed-RNN inference reaches a super real-time latency of 10-400µs with FPGA implementation.

Shi, Runbin↗
DOE OSTI2024

Moiré Patterns in Pt Overlayers on Gold: A Graph Neural Network Interatomic Potential Study

Overlayer structures in bimetallic catalysts are relevant to a variety of catalytic reactions, particularly in electrocatalysis for fuel cell applications. Previous computational studies largely consider these overlayer structures to be those of a pseudomorphic overlayer, where there is a 1:1 atomic ratio between the overlayer and the support metal. Our previous work based on density functional theory (DFT) has shown that there exist nonstoichiometric overlayer structures that are more stable than the stoichiometric ones. Here, in this work, we developed a graph neural network interatomic potential (GNN-IP) to analyze structures formed in Pt overlayers on Au(111). The GNN-IP was used to explore properties of nonstoichiometric overlayers at length scales that are prohibitively expensive to pursue with planewave DFT calculations. In particular, we examined large Pt islands on top of a 48 × 48 Au(111) unit cell to explore the influence of the rotational angle (α) between the Pt overlayer and support Au(111) on both the stability of and the preferred atomic density in the overlayer. Island structures with smaller rotational angles between the Pt overlayer and Au(111) tend to be more stable. Further, smaller rotational angles tend to result in lower atomic density in the Pt overlayer.

chemical structure↗
DOE OSTI2025

Electroreduction-Driven Formation and Connectivity of Polyoxometalate Coordination Networks

We present the synthesis of metal oxide coordination networks based on Preyssler-type polyoxoanions ([NaP 5 W 30 O 110 ] 14– and [NaP 5 MoW 29 O 110 ] 14– ) bridged with metal–aquo complexes ([M(H 2 O) n ] m+ , M m+ = Co 2+ , Ni 2+ , Zn 2+ , Y 3+ ), induced by electrochemical reduction. Networks bridged with first-row transition metals are isostructural with a previously reported Co-bridged structure, while the Y 3+ -bridged structure is new. All networks feature an uncommon binding motif of the metal cation to the oxygen atoms at cap positions, which we hypothesize is due to increased electron density at the cap upon reduction. Oxidation of a Zn 2+ -bridged network resulted in a new structure in which Zn 2+ –O cap bonds are lost, indicating the importance of reduction in the connectivity of these polyoxometalate-based coordination networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Negative Charge Confined Amine Carriers within the Nanowire Network for Stable and Efficient Membrane Carbon Capture

Membrane-based carbon dioxide (CO 2 ) capture has attracted great research interest owing to its potential for higher separation efficiency and lower energy consumption. However, it is still a challenging task to capture CO 2 with membrane from flue gas, especially under moderate-temperature and high-humidity conditions. Here, a stable CO 2 -selective membrane that can operate at temperatures up to 90 °C and under high humidity is reported. The positively charged amine carriers for CO 2 are confined within the negatively charged polymer modified carbon nanotube (CNT) network. In this structure, interconnected CNTs act as the framework for the selective layer and provide numerous nanochannels for gas transport. The negatively charged polymer ensures the carrier stability and further regulates the size of nanochannels in the CNT network. By virtue of carrier-facilitated transport, high CO 2 permeance (up to 3300 gas permeation units) and high CO 2 /N 2 selectivity (400) are achieved under simulated flue gas conditions. Moreover, theoretical calculations verify that the stable separation performance is due to the strong electrostatic interaction between the amine carriers and polymer matrix. The high performance and good stability indicate the great potential of this novel membrane structure for practical application in CO 2 capture.

36 MATERIALS SCIENCE↗