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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Improved semiclassical model for real-time evaporation of matrix black holes

In this paper, we study real-time classical matrix mechanics of a simplified 2 × 2 matrix model inspired by the black hole evaporation problem. This is a step towards making a quantitative model of real-time evaporation of a black hole, which is realized as a bound state of D0-branes in string theory. The model we study is the reduction of Yang–Mills in 2 + 1 dimension to 0 + 1 dimensions, which has been corrected with an additional potential that can be interpreted as a zero-point energy for fermions. Our goal is to understand the lifetime of such a classical bound state object in the classical regime. To do so, we pay particular attention to when D-particles separate to check that the “off-diagonal modes” of the matrices become adiabatic and use that information to improve on existing models of evaporation. It turns out that the naive expectation value of the lifetime with the fermionic correction is infinite. This is a logarithmic divergence that arises from very large excursions in the separation between the branes near the threshold for classical evaporation. The adiabatic behavior lets us get some analytic control of the dynamics in this regime to get this estimate. This divergence is cutoff in the quantum theory due to quantization of the adiabatic parameter, resulting in a long lifetime of the bound state, with a parametric dependence of order log(1/ℏ).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Understanding dynamic changes in live cell adhesion with neutron reflectometry

In this study, neutron reflectometry (NR) was used to examine various live cells' adhesion to quartz substrates under different environmental conditions, including flow stress. To the best of our knowledge, these measurements represent the first successful visualization and quantization of the interface between live cells and a substrate with sub-nanometer resolution. In our first experiments, we examined live mouse fibroblast cells as opposed to past experiments using supported lipids, proteins, or peptide layers with no associated cells. We continued the NR studies of cell adhesion by investigating endothelial monolayers and glioblastoma cells under dynamic flow conditions. We demonstrated that neutron reflectometry is a powerful tool to study the strength of cellular layer adhesion in living tissues, which is a key factor in understanding the physiology of cell interactions and conditions leading to abnormal or disease circumstances. Continuative measurements, such as investigating changes in tumor cell — surface contact of various glioblastomas, could impact advancements in tumor treatments. In principle, this can help us to identify changes that correlate with tumor invasiveness. Pursuit of these studies can have significant medical impact on the understanding of complex biological problems and their effective treatment, e.g. for the development of targeted anti-invasive therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Gaussian null coordinates, near-horizon geometry and conserved charges on the horizon of extremal nondilatonic black p-branes

In this paper, we examine the emergence of conserved charges on the horizon of a particular class of extremal nondilatonic black [Formula: see text]-branes (which reduce to extremal dilatonic black holes in [Formula: see text] dimensions upon toroidal compactification) in the presence of a probe massless scalar field in the bulk. This result is achieved by writing the black [Formula: see text]-brane geometry in a Gaussian null coordinate system which allows us to get a nonsingular near-horizon geometry description. We find that the near-horizon geometry is [Formula: see text] and that the [Formula: see text] section has an internal structure which can be seen as a warped product of [Formula: see text] in Gaussian null coordinates. We show that the bulk scalar field satisfying the field equations is expanded in terms of nonnormalizable and normalizable modes, which for certain suitable quantization conditions are well-behaved at the boundary of [Formula: see text] space. Furthermore, we show that picking the normalizable modes results in the existence of conserved quantities on the horizon. We discuss the impact of these conserved quantities in the late-time regime.

Astronomy & Astrophysics↗
DOE OSTI2020

The noise of gravitons

We show that when the gravitational field is treated quantum-mechanically, it induces fluctuations — noise — in the lengths of the arms of gravitational wave detectors. Here, the characteristics of the noise depend on the quantum state of the gravitational field and can be calculated exactly in several interesting cases. For coherent states, the noise is very small, but it can be greatly enhanced in thermal and (especially) squeezed states. Detection of this fundamental noise would constitute direct evidence for the quantization of gravity and the existence of gravitons.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

CEAZ: Accelerating Parallel I/O Via Hardware-Algorithm Co-Designed Adaptive Lossy Compression

As supercomputers continue to grow to exa-scale, the amount of data that needs to be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors to reduce the data size and improve the I/O performance. However, little work has been done for effectively offloading lossy compression onto FPGA-based SmartNICs to reduce the compression overhead. In this paper, we propose a hardware-algorithm co-design of efficient and adaptive lossy compressor for scientific data on FPGAs (called CEAZ) to accelerate parallel I/O. Our contribution is fourfold: (1) We propose an efficient Huffman coding approach that can adaptively update Huffman codewords online based on codewords generated offline (from a variety of representative scientific datasets). (2) We derive a theoretical analysis to support a precise control of compression ratio under an error-bounded compression mode, enabling accurate offline Huffman codewords generation. This also help us create a fixed-ratio compression mode for consistent throughput. (3) We develop an efficient compression pipeline by adopting cuSZ’s dual-quantization algorithm to our hardware use case. (4) We evaluate CEAC on five real-world datasets with both a single FPGA board and 256 nodes from Bridges2 supercomputer. Experiments show that CEAZ outperforms the second-best FPGA-based lossy compressor by 2× of throughput and 9.6× of compression ratio. It also improves MPI_File_write and MPI_Gather throughputs by up to 32.7× and 31.4×, respectively.

Zhang, Chengming↗
DOE OSTI2025

Fractional Quantum Anomalous Hall Effect

The realization of the fractional quantum anomalous Hall effect (FQAHE) in a zero-field fractional Chern insulator is a new advancement in condensed matter physics, resulting from the interplay among strong correlations, topology, and spontaneous time-reversal symmetry breaking in lattice systems. In this review, we highlight the experimental and theoretical progress toward achieving FQAHE in two material platforms: twisted bilayer MoTe 2 and rhombohedral-stacked multilayer graphene. These systems host narrow topological bands with nontrivial Chern numbers, enabling interaction-driven fractionalized states analogous to the fractional quantum Hall effect, but without external magnetic fields. We discuss how spontaneous ferromagnetism, moiré lattice reconstruction, and band topological effects underpin the emergence of FQAHE in twisted MoTe 2 . We describe experimental discoveries of zero-field fractional Chern insulators in both transport and optical experiments, as well as signatures of composite Fermi liquids and higher-energy Chern band, which may shed light on engineering nonabelian states. In rhombohedral graphene/hexagonal boron nitride moiré superlattices, we review the recent observations of fractionally quantized Hall resistance, connections between FQAHE and extended quantum anomalous Hall phases, and the coexistence of superconductivity and FQAHE. Furthermore, these discoveries not only deepen our understanding of strongly correlated topological matter but also open new frontiers for exploring nonabelian anyons, fault-tolerant quantum computation, and topological opto-spintronics free of magnetic fields.

bilayer↗
DOE OSTI2013

Time-Dependent Density Functional Theory and the Real-Time Dynamics of Fermi Superfluids

Here, I describe the time-dependent superfluid local density approximation, which is an adiabatic extension of the density functional theory to superfluid Fermi systems and their real-time dynamics. This new theoretical framework has been used to report on several phenomena in cold atomic gases and nuclear collective motion: excitation of the Higgs modes in strongly interacting Fermi superfluids, generation of quantized vortices, crossing and reconnection of vortices, excitation of the superflow at velocities above the critical velocity, excitation of quantum shock waves and domain walls in the collisions of superfluid atomic clouds, and excitation of collective states in nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

LEMD

Deep neural network-based force field enables researchers to study materials systems of large size and/or time scale efficiently with comparable accuracy to DFT-based methods. However, such force fields, if improperly trained or mistakenly selected, could result in ill-conditioned results. This SBIR Phase I project has explored the possibility to characterize and index force fields in Deep potential theme by quantizing local atomic environments of atoms in a simulation system. This project has also demonstrated the feasibility of building a public accessible web infrastructure providing high quality machine learning algorithm-based force field for next generation materials modeling and simulation to accelerate the study and discovery of new energy-related materials.

Su, Ninghai↗
DOE OSTI2024

Generic Multi-Layer Perceptron Inference Accelerator on FPGA (vneuron) v1.0

We have designed and implemented a neural network inference compute engine (vneuron) that can be deployed in the fabric of any FPGA without using special hardware accelerator primitive. The "vneuron" is purely written in verilog, and supports scalable neural network structure with fully connected layers and ReLU activation ( Multi-Layer Perceptron architecture) with 16 bits of precision. We have demonstrated it on an Xilinx Artix 7 FPGA for a 16-input, 8-output MLP with 3 layer, 1600 parameters. It takes 40 DSP48E and 40 BRAM18, and takes 131 clock cycles for computing (1048 ns when clocked at 125MHz). We include PyTorch quantization from a given floating point model, and provide behavioral verification simulation in the disclosed software package.

Du, Qiang↗
DOE OSTI2024

prune_quant_vit

This tool provides codes to prune and quantize vision transformers (ViTs). The tool will allow developers and researchers to speed up inference of ViTs and deploy them on CPUs.

Bhardwaj, Kshitij↗
DOE OSTI2024

Sentinel

Network intrusion detection systems (NIDS) are commonplace in network security but they frequently employ algorithms that are computational demanding requiring hardware and software with significant power requirements. Two examples of such resource-intensive algorithms used for network security are regular expression matching and broader signature pattern matching which are commonly used in deep packet inspection (DPI). Network security algorithms that have large power requirements may be a challenge for low-power internet-of-things (IoT) environments, which generally lack the power resources to implement complex security measures like computationally expensive DPI at the edge. Furthermore, IoT environments incorporating 5G standalone networks have network latency constraints beyond just power that make DPI at the edge even more difficult. Programmable logic is ideally suited for machine learning inference for DPI because of its deep instruction level parallelism and single-cycle memory access. Machine learning approaches for DPI have been explored before using the programmable logic of field programmable gate arrays (FPGA) as a potential solution for NIDS approaches that would be power-suitable for IoT. However, those previous programmable logic NIDS approaches utilize either a supervised or unsupervised learning model. Sentinel utilizes the ensemble of these two machine learning approaches known as a semi-supervised approach which has shown promise in NIDS implementations. Sentinel provides a programmable logic implementation of a semi-supervised approach for DPI which operates at much lower power and latency than a GPU implementation with negligible loss of accuracy due to quantization through a logistic regressor.

Anderson, MatthewW [Idaho National Laboratory (INL↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2025

CODARcode/MGARD

MGARD is a software providing error-controlled lossy compression and data refactoring based on multi-grid theories. It transforms floating-point scientific data into a multilevel representation, followed by quantization and lossless encoding processes, resulting in a self-describing compressed buffer. It supports diverse data topologies, error control norms, and computing architectures.

Chen, Jieyang [University of Oregon]↗
DOE OSTI2026

PCA -BASED COMPRESSOR HARDWARE DESIGN GENERATOR IN CHISEL

SF-25-077 This repository includes a hardware generator written in Chisel that generates lossy compression hardware designs based on principal component analysis (PCA), along with a flexible testbench. It also includes a Python tool for evaluating the accuracy loss of integer quantization.

Kazutomo, Yoshi [Argonne National Laboratory (ANL)↗
DOE OSTI2022

Spectroscopic Investigation of Catalyst Inks and Thin Films Toward the Development of Ionomer Quality Control

As the production of polymer electrolyte fuel cells expands, novel quality control methods must be invented or adapted in order to support expected rates of production. Ensuring the quality of deposited catalyst layers is an essential step in the fuel cell manufacturing process, as the efficiency of a fuel cell is reliant on the catalyst layer being uniform at both the target platinum loading and the target ionomer content. Implementing a quality control method that is sensitive to these aspects is imperative, as wasting precious metals and other catalyst materials is expensive, and represents a potential barrier to entry into the field for manufacturers experimenting with novel deposition processes. In this work, we analyzed catalyst inks to determine if their ionomer content could be quantized spectroscopically. Attenuated total reflection (ATR) Fourier transform infrared spectroscopic technique was investigated producing a signal proportional to the ionomer content. ATR spectroscopy was able to quantitatively differentiate samples in which the ionomer to carbon mass ratio (I/C) varied between 0.9 and 3.0. The I/C ratio was correlated to the measured ATR signal near the CF 2 vibrational bands located between 1100 cm −1 and 1400 cm −1 . The experimental results obtained constitute a step toward the development of novel quality control methodologies for catalyst inks utilized by the fuel cell industry.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2023

Black-box statistical prediction of lossy compression ratios for scientific data

Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression, no theoretical or data-based quantification of lossy compressibility exists for scientific data. Users rely on trial and error to assess lossy compression performance. As a strong data-driven effort toward quantifying lossy compressibility of scientific datasets, we provide a statistical framework to predict compression ratios of lossy compressors. Our method is a two-step framework where (i) compressor-agnostic predictors are computed and (ii) statistical prediction models relying on these predictors are trained on observed compression ratios. Proposed predictors exploit spatial correlations and notions of entropy and lossyness via the quantized entropy. We study 8+ compressors on 6 scientific datasets and achieve a median percentage prediction error less than 12%, which is substantially smaller than that of other methods while achieving at least a 8.8× speedup for searching for a specific compression ratio and 7.8× speedup for determining the best compressor out of a collection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Robustness of helical edge states under edge reconstruction

Abstract The helical edge states of time-reversal invariant two-dimensional topological insulators are protected against backscattering in idealized models. In more realistic scenarios with a shallow confining potential at the sample boundary, additional strongly interacting edge states may arise, that could interfere with the topological protection of edge conduction. We find that interaction effects within the reconstructed edges are well described by the Luttinger liquid model. While interactions between this Luttinger liquid and the helical edge states can in principle give rise to dynamical spin polarization and the breaking of time-reversal symmetry, we demonstrate that random spin-orbit coupling strongly suppresses such dynamical spin polarization, resulting in the persistence of near quantized edge conduction.

John, Niels↗
DOE OSTI2022

Thermal Neutron Scattering Law Evaluation for Zirconium Carbide and Critical Mass Calculations

Zirconium carbide (ZrC) is a candidate material for use in advanced high temperature reactors, including space nuclear thermal propulsion applications. Thermal neutron scattering laws (TSLs) are generated for carbon bound in ZrC, C(ZrC), and zirconium bound in ZrC, Zr(ZrC), using ab initio lattice dynamics methods. These evaluations are to be submitted for inclusion in ENDF/B-VIII.1 and use the incoherent approximation for inelastic scattering as well as the new mixed elastic scattering treatment. The application of disordered alloy theory is introduced to appropriately capture the isotopic composition of Zr and C in the elastic scattering cross section. Localized higher energy vibrations in the C(ZrC) phonon density of states that are separated from lower energy modes result in quantized oscillations in the inelastic contributions to the TSL with a significant likelihood of large energy down-scattering and up-scattering interactions, where the latter increases in probability with temperature. The quanta of energy transfer during neutron thermalization is substantially greater than classically expected within the thermal neutron energy range. MC21 critical mass calculations of ZrC mixtures with high-enriched uranium demonstrate an impact of the TSLs when compared to free-gas treatment for 235 U concentrations less than 0.2 g/cm 3 . Additional MC21 critical mass calculations with homogenous mixtures of ZrC and reactor-grade graphite also demonstrate sensitivity to the ZrC TSL for thermal spectrum driven fission systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗