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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Building a controlled-NOT gate between polarization and frequency

By harnessing multiple degrees of freedom (DoFs) within a single photon, controlled quantum unitaries, such as the two-qubit controlled-NOT ( cnot ) gate, play a pivotal role in advancing quantum communication protocols such as dense coding and entanglement distillation. In this work, we devise and realize a cnot operation between polarization and frequency DoFs by exploiting directionally dependent electro-optic phase modulation within a fiber Sagnac loop. Alongside computational basis measurements, we validate the effectiveness of this operation through the synthesis of all four Bell states in a single photon, all with fidelities greater than 98%. This demonstration opens new avenues for manipulating hyperentanglement across these two crucial DoFs, marking a foundational step toward leveraging polarization-frequency resources in fiber networks for future quantum applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment. SNAC-Pack combines Neural Architecture Codesign's multi-stage search capabilities with the Resource Utilization and Latency Estimator, enabling multi-objective optimization across accuracy, FPGA resource utilization, and latency without requiring time-intensive synthesis for each candidate model. We demonstrate SNAC-Pack on a high energy physics jet classification task, achieving 63.84% accuracy with resource estimation. When synthesized on a Xilinx Virtex UltraScale+ VU13P FPGA, the SNAC-Pack model matches baseline accuracy while maintaining comparable resource utilization to models optimized using traditional BOPs metrics. This work demonstrates the potential of hardware-aware neural architecture search for resource-constrained deployments and provides an open-source framework for automating the design of efficient FPGA-accelerated models.

Weitz, Jason [UC, San Diego] (ORCID:00090004631535↗
DOE OSTI2026

Synthesis of boron-carbide aerogels

We present the synthesis of boron carbide aerogels utilizing nano-boron powder and resorcinol–formaldehyde (RF) organic aerogels as precursors. Monolithic aerogels were fabricated from suspensions of boron nanoparticles and RF via an organic sol-gel process, enabling effective distribution of boron in the gel network. The resulting gels underwent supercritical drying, thermal reduction, and subsequent heat treatment to yield boron carbide aerogels with densities ranging from 37 to 55 mg/cm³. By tuning the boron-to-carbon ratio, heat treatment temperature, and dwell time, surface areas up to 53 m²/g were obtained. X-ray diffraction analysis confirmed the formation of the boron carbide phase and detected the presence of residual carbon within the structure.

Materials science↗
DOE OSTI2025

Propagating synthetic populations with dynamic Bayesian networks: a framework for long-horizon demographic forecasting

This study presents a dynamic demographic microsimulator using dynamic Bayesian networks to forecast long–term changes in household and individual life events. Leveraging longitudinal Panel Study of Income Dynamics (PSID) data, two networks for individuals and households were modeled to simulate transitions in employment, income, education, marriage, childbirth, leaving the parental home, home ownership, mortality, and household formation or dissolution. Across 1,000 simulation runs spanning 24 years, household–level outcomes remain highly accurate and individual–level predictions reasonable. Although accuracy naturally declines with projection horizon, performance remains promising at both levels. This study addresses a key limitation of existing population synthesis models, which typically generate only a single static snapshot of the population. In conclusion, by introducing a framework that propagates cross-sectional outputs into the future, the microsimulator enables the tracking of demographic evolution over time, enhances realism in population-based simulations, and supplies credible inputs to agent-based travel demand models.

Demographic modeling↗
DOE OSTI2026

Achieving ultrahigh modulus of resilience and enhanced thermal stability in ZnO x /SU-8 interpenetrating network polymer nanocomposite nanopillars

The modulus of resilience, a mechanical property that quantifies the maximum strain energy density a material can store during elastic deformation, is a crucial parameter for materials used in flexible displays, micro/nano-electro-mechanical system (M/NEMS) actuators, and ultra-sensitive pressure sensors. In this study, ZnO x /SU-8 nanocomposite nanopillars with a diameter of 300 nm, fully infiltrated with a uniformly distributed, interpenetrating amorphous ZnO x filler network, were synthesized via vapor-phase infiltration (VPI). In-situ uniaxial nano-compression tests revealed that the modulus of resilience of ZnO x /SU-8 reaches ∼ 12 MJ/m 3 , which is an ultrahigh value among all engineering materials with comparable strength. In addition, the synthesis fidelity, inorganic infiltration depth, and mechanical performance were all significantly improved compared to VPI-synthesized AlO x nanocomposites. Thermal stability, another key requirement for M/NEMS device materials operating under extreme environments, was also notably enhanced. Furthermore, partial crystallization of the amorphous ZnO x fillers during annealing contributed to an additional increase in modulus of resilience, reaching up to ∼ 13.9 MJ/m 3 . This work presents an effective fabrication strategy for producing nanostructured organic–inorganic hybrid nanocomposites with ultrahigh modulus of resilience and superior thermal stability, paving the way for their integration into next-generation flexible displays and high-performance M/NEMS devices working under harsh environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancing stability, magnetic anisotropy, and coercivity of manganese aluminum: Machine learning, ab initio , and micromagnetic modeling

The binary manganese aluminum (MnAl) alloy with L⁢1 0 crystal structure is a promising rare earth (RE) element-free permanent magnetic material because of its exceptional magnetic properties. However, experimentally synthesizing it in a stable bulk form is extremely challenging. Here, in this study, an alternative method of stabilizing the material, a pathway for experimental synthesis and validation, is proposed and theoretically verified. This is done by partially substituting Mn and Al sites with Fe and Ni and identifying its enhanced phase stability, saturation magnetization density, magnetic anisotropy, and coercivity from density functional theory (DFT), machine learning (ML) crystal graph convolution neural network (CGCNN), and micro-magnetic modeling. When considering a fixed 50% Ni, the magnetic anisotropy increases with the increasing Fe content but decreases the formation energy. The calculated formation energies, elastic constants, and phonon frequencies demonstrate that the binary and quaternary compositions are stable. Most importantly, in 50% Fe and Ni-substituted-equiatomic phase, magnetic anisotropy constants and saturation magnetization density increase by 56% and 23% as compared to the MnAl. Further, the coercivity of the equiatomic phase predicted with micro-magnetic modeling is higher by 17% than the parent compound.

Bhandari, Churna [Ames National Laboratory, and Io↗
DOE OSTI2026

Agentic traffic intelligence: Augmented human-in-the-loop scenario generation for microscopic traffic simulation

Traditional microscopic traffic simulation generation often relies on static datasets and manual design, limiting its ability to simulate complex conditions easily. This paper presents a novel framework, Agentic Traffic Intelligence, which combines human approval large language models (LLMs), the Real-Twin tool, and multi-agent systems to perform realistic microscopic traffic simulation scenario generation. The proposed framework incorporates human-in-the-loop (HIL) control, retrieval-augmented generation (RAG), and multi-agent control mechanisms. HIL mechanisms are used to guide multiple LLMs focused on attributes for microscopic simulation generation and to improve the interpretability and transparency of LLM execution for users. RAG enhances context extraction by dynamically integrating external knowledge sources for traffic scenario generation foundations. A multi-agent architecture with supervisory control coordinates the interaction of simulation components, including traffic simulators, control logic, and calibration tools. This enables the synthesis of simulation-ready scenarios that reflect dynamic demand profiles and behavior controls. Furthermore, the framework fuses multisource traffic data with unstructured context and supports iterative refinement through interactive user feedback. Validated through microscopic simulation using Simulation of Urban Mobility, the generated scenarios demonstrate high-fidelity network generation with inflow and turn movement and behavioral calibration, offering a robust and efficient tool for stress-testing and optimizing urban mobility systems.

Hierarchical multi-agent control↗
DOE OSTI2025

A stable 15-member bacterial SynCom promotes Brachypodium growth under drought stress

Introduction: Rhizosphere microbiomes are known to drive soil nutrient cycling and influence plant fitness during adverse environmental conditions. Field-derived robust Synthetic Communities (SynComs) of microbes mimicking the diversity of rhizosphere microbiomes can greatly advance a deeper understanding of such processes. However, assembling stable, genetically tractable, reproducible, and scalable SynComs remains challenging. Methods: Here, we present a systematic approach using a combination of network analysis and cultivation-guided methods to construct a 15-member SynCom from the rhizobiome of Brachypodium distachyon. This SynCom incorporates diverse strains from five bacterial phyla. Genomic analysis of the individual strains was performed to reveal encoded plant growth-promoting traits, including genes for the synthesis of osmoprotectants (trehalose and betaine) and Na+/K+ transporters, and some predicted traits were validated by laboratory phenotypic assays. Results: The SynCom demonstrates strong stability both in vitro and in planta. Most strains encoded multiple plant growth-promoting functions, and several of these were confirmed experimentally. The presence of osmoprotectant and ion transporter genes likely contributed to the observed resilience of Brachypodium to drought stress, where plants amended with the SynCom recovered better than those without. We further observed preferential colonization of SynCom strains around root tips under stress, likely due to active interactions between plant root metabolites and bacteria. Discussion: Our results demonstrate that trait-informed construction of synthetic communities can yield stable, functionally diverse consortia that enhance plant resilience under drought. Preferential colonization near root tips points to active, localized plant-microbe signaling as a component of stress-responsive recruitment. This stable SynCom provides a scalable platform for probing mechanisms of plant-microbe interaction and for developing microbiome-based strategies to improve soil and crop performance in variable environments.

Yadav, Archana↗
DOE OSTI2026

Anisotropic magnetism and Kondo-lattice behavior in the frustrated antiferromagnet Ce 3 ⁢MgBi 5

Here, we report the synthesis and physical characterization of single-crystalline Ce 3 ⁢MgBi 5 , a previously unexplored member of the Ce 3 ⁢𝑀⁢𝑃⁢𝑛 5 family. This compound crystallizes in the hexagonal 𝑃⁢6 3 /𝑚⁢𝑐⁢𝑚 structure, featuring an anisotropic Ce sublattice composed of zigzag chains along the 𝑐 axis and a distorted kagome-like network in the basal plane. Magnetization measurements reveal antiferromagnetic order below 𝑇 𝑁 ≈ 4.2K with strong magnetic anisotropy and multiple field-induced metamagnetic transitions for fields applied perpendicular to [001], leading to a dome-shaped 𝐻–𝑇 phase diagram. Electrical transport exhibits characteristic signatures of a Ce-based Kondo lattice, including broad resistivity maxima and pronounced field-dependent anomalies in the magnetoresistance and Hall response that track the magnetic phase boundaries. Specific-heat measurements confirm the magnetic transition and show that the full R ⁢ln⁡ 2 entropy expected for a Ce 3+ Kramers doublet is recovered by 20 K, indicating an extended temperature range of magnetic fluctuations consistent with Kondo correlations. Our results establish Ce 3 ⁢MgBi 5 as a platform within the Ce 3 ⁢𝑀⁢𝑃⁢𝑛 5 family for exploring the interplay of geometric frustration, magnetic anisotropy, and Kondo-lattice physics under applied magnetic fields.

Kondo effect↗
DOE OSTI2025

Atomic-scale structure of ZrO 2 : Formation of metastable polymorphs

Metastable phases can exist within local minima in the potential energy landscape when they are kinetically “trapped” by various processing routes, such as thermal treatment, grain size reduction, chemical doping, interfacial stress, or irradiation. Despite the importance of metastable materials for many technological applications, little is known about the underlying structural mechanisms of the stabilization process and atomic-scale nature of the resulting defective metastable phase. Investigating ion-irradiated and nanocrystalline zirconia with neutron total scattering experiments, we show that metastable tetragonal ZrO 2 consists of an underlying structure of ferroelastic, orthorhombic nanoscale domains stabilized by a network of domain walls. The apparent long-range tetragonal structure that can be recovered to ambient conditions is only the configurational ensemble average of the underlying orthorhombic domains. This structural heterogeneity with a distinct short-range order is more broadly applicable to other nonequilibrium materials and provides insight into the synthesis and recovery of functional metastable phases with unique physical and chemical properties.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

SPARTA: High-Level Synthesis of Parallel Multi-Threaded Accelerators

This article presents a methodology for the Synthesis of PARallel multi-Threaded Accelerators (SPARTA) from OpenMP annotated C/C++ specifications. SPARTA extends an open-source HLS tool, enabling the generation of accelerators that provide latency tolerance for irregular memory accesses through multithreading, support fine-grained memory-level parallelism through a hot-potato deflection-based network-on-chip (NoC), support synchronization constructs, and can instantiate memory-side caches. Our approach is based on a custom runtime OpenMP library, providing flexibility and extensibility. Experimental results show high scalability when synthesizing irregular graph kernels. The accelerators generated with our approach are, on average, 2.29x faster than state-of-the-art HLS methodologies.

Design automation↗
DOE OSTI2026

Catalytic conversion of cellulose and its derived sugars to 5-Hydroxymethylfurfural, levulinate esters, and sorbitol: a comprehensive review

Cellulose, an abundant, renewable, and sustainable non-edible carbon source from agriculture and forestry, has attracted great attention for producing diverse value-added chemicals and fuels. However, the rigid 3D structure of cellulose, maintained by an extensive hydrogen bonding network, hinders chemical conversion, requiring effective pretreatment to break down the crystalline structure. High-value cellulose-derived compounds such as 5-hydroxymethylfurfural (5-HMF), levulinate esters, and sorbitol, recognized as critical platform chemicals by the U.S. Department of Energy, are particularly attractive for versatile applications. This review provides a comprehensive overview of methodologies for the chemical synthesis of 5-HMF, levulinate esters, and sorbitol, focusing on direct catalytic conversion of cellulose. It delves into recent advancements in reaction systems and catalysts, highlighting catalytic pathways, selectivity, strategies for process optimization, and computational approaches, while discussing the challenges associated with the catalytic conversion of cellulose into these high-value products and offering potential strategies for enhancing future catalytic processes.

Huang, Kaixuan [Yancheng Teachers Univ. (China); N↗
DOE OSTI2026

Low-energy 17 O(𝑛,𝛾)⁢ 18 O reaction within the microscopic potential model and its role for the weak 𝑟 process

The neutron radiative capture reaction 17 O ⁡(𝑛,𝛾) ⁢18 O plays a pivotal role in both nuclear structure studies and astrophysical nucleosynthesis, particularly in the formation of elements during hydrostatic and explosive stellar environments. We calculated the 17 O ⁡(𝑛,𝛾) ⁢18 O cross section within the Skyrme Hartree-Fock potential model and analyzed electric dipole 𝐸⁢1 transitions to both positive- and negative-parity states below the α-decay threshold in 18 O. Our cross sections are significantly different from the data available in commonly used libraries. We further investigate the impact of the new calculated cross section on weak 𝑟-process nucleosynthesis using large-scale reaction network calculations across a wide range of electron fractions and entropies. Our results show that the 17 O ⁡(𝑛,𝛾) ⁢18 O reaction rate significantly influences the production of first 𝑟-process peak elements, such as strontium, under specific astrophysical conditions. This study highlights the importance of accurate nuclear data for light isotopes in modeling heavy-element synthesis and provides updated reaction rates for future nucleosynthesis simulations.

6 ≤ A ≤ 19↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2024

Dielectric and magnetic properties of microwave-absorbing FeAl x O y catalysts fabricated via solution combustion synthesis

Iron-based alumina (FeAl x O y ) nanocomposites are microwave-absorbers and catalysts, which makes them promising for emerging microwave-assisted thermocatalytic technologies. Solution combustion synthesis (SCS) has been used to synthesize FeAl x O y powders, and prior work has demonstrated that adjusting SCS parameters significantly changes phase composition and specific surface area of the products. However, it is unclear how synthesis parameters affect their microwave-absorbing properties, which are essential for optimizing microwave-assisted technologies. To address this challenge, in the present work, twelve different FeAl x O y products were synthesized at different combinations of the SCS parameters such as two fuels (citric acid and glycine), two heating modes (hotplate and muffle furnace), and three Fe:Al molar ratios (2:1, 1:1, 1:2). Dielectric and magnetic properties of the products were characterized using a network analyzer and a vibrating sample magnetometer. Based on the measured permittivity and permeability, penetration depth and reflection loss were calculated as a function of frequency and bed thickness. The products were heated by microwaves at 2.45 GHz and then examined with X-ray diffraction (XRD) analysis. For all products, the magnetic saturation was lower than for bulk iron oxides because of the small crystallite size and aluminum substitution. The use of glycine induced high dielectric losses and enabled fast microwave-heating rates compared to citric acid. Higher Fe:Al ratio also led to higher dielectric and magnetic losses. With glycine fuel, SCS in a furnace induced larger penetration depth and lower microwave absorption than SCS on a hotplate. The minimization of reflected power was more sensitive to the thickness of the product bed than to the frequency of the electromagnetic field. Post-heating XRD analysis revealed different phase transformations in the FeAl x O y powders depending on the SCS parameters. As a result, an FeAl x O y material, synthesized via incipient wetness impregnation, lacked magnetic losses and did not heat well as compared to the SCS products.

Combustion synthesis↗
DOE OSTI2026

Effects of Synthesis Conditions on the Structure and Conductivity of Hydrogen-Substituted Graphdiyne

This study investigates how synthesis conditions influence the structure and conductivity of hydrogen-substituted graphdiyne (HsGDY). By varying the reaction temperature and solvent, we find that small changes in conditions markedly affect triple-bond retention and electronic continuity. Solid-state 13 C NMR and Raman spectroscopy reveal that elevated temperatures drive alkyne loss and partial graphitization, with N,N-dimethylformamide (DMF) promoting faster degradation than pyridine. The resulting decline in alkyne content directly correlates with reduced conductivity, indicating that preserving conjugation is essential for charge transport. These findings clarify how the synthetic environment governs the structural and electronic evolution of graphdiyne frameworks, providing insight into the controlled preparation of conjugated carbon networks.

alkyne retention↗
DOE OSTI2026

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multiple efforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680,000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin [Fermilab] (ORCID:0000000157000288↗
DOE OSTI2025

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multipleefforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of synthesized ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680 000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin G. [Fermilab]↗