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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 OSTI2020

Application of laser-driven capacitor-coil to target normal sheath acceleration

A laser-driven accelerator generates protons with tens of MeV in energy by a compact, strong, and transient accelerating electric field produced as a result of laser–plasma interactions at relativistic intensities. In previous studies, two- and three-dimensional particle-in-cell simulations revealed that the application of a kT-level axial magnetic field results in an enhancement of proton acceleration via the target normal sheath acceleration mechanism due to reduced lateral electron divergence and improved electron heating efficiency. An experimental investigation of this scheme on the GEKKO-XII and the LFEX facilities found that the number and maximum energy of the accelerated protons decreased with increasing the temporal delay between the pulse driving the external magnetic-field and the pulse accelerating the protons, contrary to the theoretical and numerical expectations. Finally, we identify sources responsible for the degradation of the proton beam performance and we propose an alternative experimental setup to mitigate the degradation in future experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Electron lenses: historical overview and outlook

The first electron lenses — understood as "lenses made of electrons" rather than "lenses to focus electrons" — were envisioned in the mid-1990s and built in the early 2000s for compensation of beam-beam effects in the Tevatron proton-antiproton collider. Since then, the lenses — a novel instrument for high-energy particle accelerators — have been added to the toolbox of modern beam facilities, being particularly useful for the energy frontier superconducting hadron colliders ("supercolliders"). In this article we briefly present the history of ideas and developments toward effective use of low-energy high-current bright electron beams in high energy accelerators and discuss the promise of their future applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Electrically Accelerated Mechanochemical Film Formation by a Phosphonium Phosphate Ionic Liquid: An In Situ Chemical Kinetics Investigation

Powertrains in electric vehicles are exposed to stray currents that accelerate wear and cause failure of mechanical components. These durability issues are further aggravated when using low-viscosity lubricants, which are desired for energy efficiency but create harsher contact conditions at sliding interfaces. This study investigates a phosphonium phosphate ionic liquid as a performance-enhancing additive in a low-viscosity base oil for lubricating electrified sliding interfaces. Ionic liquids can adsorb and react on contact interfaces via stress-assisted chemical reactions, generating nanometric tribofilms that provide protection against wear. However, the effect of electric fields on the mechanochemistry of ionic liquids is poorly understood, hindering their adoption in lubricants for electrified powertrains. This article reports an in situ optical interferometry study of ionic liquid derived tribofilm growth kinetics at stressed sliding/rolling interfaces under direct currents. The application of electric currents accelerated tribofilm formation up to a critical current density (∼1.4 A/mm 2 ), beyond which pitting-induced wear dominated. The tribofilms were composed of iron phosphates, iron oxides, and carbon species, with iron oxides becoming predominant under applied currents. These tribofilms prevented the scuffing failure of steel surfaces under electrified conditions. Based on the results, a kinetic model is proposed that integrates electric current effect into the classical stress-assisted thermal activation framework to allow prediction of tribofilm growth at electrified sliding contacts. This framework provides crucial guidance for designing next-generation lubricants for electrified transportation and power generation systems.

additives↗
DOE OSTI2023

Advanced Turbulence Models for Large-Scale Atmospheric Boundary Layer Flows

We present high-fidelity large-eddy-simulation (LES) modeling approaches for the turbulent atmospheric boundary layer (ABL) flows. Wind energy is a prime example of an application driven by ABL. Generation of electrical energy from farms of wind turbines at night in the stable ABL is a particularly interesting situation. In this report, we consider the well-known GEWEX (Global Energy and Water Cycle Experiment) Atmospheric Boundary Layer Study (GABLS) stably stratified benchmark LES case. We use a high-order spectral element code Nek5000/RS, which is supported under the DOE's Exascale Computing Project (ECP) Center for Efficient Exascale Discretizations (CEED) project, targeting application simulations on various acceleration-device based exascale computing platforms. In our earlier ANL report, we demonstrated our newly developed subgrid-scale (SGS) models based on high-pass filter (HPF), mean-field eddy viscosity (MFEV), and Smagorinsky (SMG) with no-slip and traction boundary conditions, provided with low-order statistics, convergence and turbulent structure analysis. In this report, we extend the range of our SGS modeling approaches in the context of the mean-field eddy viscosity (MFEV), to include the solution of an SGS turbulent kinetic energy equation (TKE). We demonstrate the model fidelity of Nek5000/RS in comparison to that of AMR-Wind, a block-structured second-order finite-volume code with adaptive-mesh-refinement capabilities, with which we studied scaling performance for both codes in comparison on DOE's leadership computing platforms.

17 WIND ENERGY↗
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 OSTI2020

Quasi‐Binary Transition Metal Dichalcogenide Alloys: Thermodynamic Stability Prediction, Scalable Synthesis, and Application

Abstract Transition metal dichalcogenide (TMDCs) alloys could have a wide range of physical and chemical properties, ranging from charge density waves to superconductivity and electrochemical activities. While many exciting behaviors of unary TMDCs have been demonstrated, the vast compositional space of TMDC alloys has remained largely unexplored due to the lack of understanding regarding their stability when accommodating different cations or chalcogens in a single‐phase. Here, a theory‐guided synthesis approach is reported to achieve unexplored quasi‐binary TMDC alloys through computationally predicted stability maps. Equilibrium temperature–composition phase diagrams using first‐principles calculations are generated to identify the stability of 25 quasi‐binary TMDC alloys, including some involving non‐isovalent cations and are verified experimentally through the synthesis of a subset of 12 predicted alloys using a scalable chemical vapor transport method. It is demonstrated that the synthesized alloys can be exfoliated into 2D structures, and some of them exhibit: i) outstanding thermal stability tested up to 1230 K, ii) exceptionally high electrochemical activity for the CO 2 reduction reaction in a kinetically limited regime with near zero overpotential for CO formation, iii) excellent energy efficiency in a high rate Li–air battery, and iv) high break‐down current density for interconnect applications. This framework can be extended to accelerate the discovery of other TMDC alloys for various applications.

Hemmat, Zahra↗
DOE OSTI2026

Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints

Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Multiphysics modeling of accelerators through code integration

This work aims to improve the ability of particle accelerator researchers to develop high-performance accelerator cavity designs by creating an overall multiphysics framework that integrates and couples existing application codes. This framework will allow accelerator researchers to build multiphysics models that will optimize cavity design, improve understanding of whole-device performance, and reduce the development and fabrication costs of accelerator research. We utilize the open-source VizSchema data standard as an intermediate data structure interface layer to standardize interfaces between individual application codes. VizScema is extensively documented online, and plugins for VizSchema are available for popular visualization packages, including VisIt and ParaView. Currently, the work focuses on coupling the EM field solver COMSOL and the electron gun code MICHELLE to allow COMSOL field-solve results to be seamlessly used by MICHELLE for particle-solve. Later work will extend this integration to include other fields, particles, and thermodynamics simulation codes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Controlling Selectivity in Plasmonic Catalysis: Switching Reaction Pathway from Hydrogenation to Homocoupling Under Visible‐Light Irradiation

Abstract Plasmonic catalysis enables the use of light to accelerate molecular transformations. Its application to the control reaction selectivity is highly attractive but remains challenging. Here, we have found that the plasmonic properties in AgPd nanoparticles allowed different reaction pathways for tunable product formation under visible‐light irradiation. By employing the hydrogenation of phenylacetylene as a model transformation, we demonstrate that visible‐light irradiation can be employed to steer the reaction pathway from hydrogenation to homocoupling. Our data showed that the decrease in the concentration of H species at the surface due to plasmon‐enhanced H 2 desorption led to the control in selectivity. These results provide important insights into the understanding of reaction selectivity with light, paving the way for the application of plasmonic catalysis to the synthesis of 1,3‐diynes, and bringing the vision of light‐driven transformations with target selectivity one step closer to reality.

Peiris, Erandi↗
DOE OSTI2022

Controlling Selectivity in Plasmonic Catalysis: Switching Reaction Pathway from Hydrogenation to Homocoupling Under Visible–Light Irradiation

Plasmonic catalysis enables the use of light to accelerate molecular transformations. Its application to the control reaction selectivity is highly attractive but remains challenging. Here, we have found that the plasmonic properties in AgPd nanoparticles allowed different reaction pathways for tunable product formation under visible-light irradiation. By employing the hydrogenation of phenylacetylene as a model transformation, we demonstrate that visible-light irradiation can be employed to steer the reaction pathway from hydrogenation to homocoupling. Our data showed that the decrease in the concentration of H species at the surface due to plasmon-enhanced H2 desorption led to the control in selectivity. Furthermore, these results provide important insights into the understanding of reaction selectivity with light, paving the way for the application of plasmonic catalysis to the synthesis of 1,3-diynes, and bringing the vision of light-driven transformations with target selectivity one step closer to reality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Evaluation of dual-weighted residual and machine learning error estimation for projection-based reduced-order models of steady partial differential equations

Projection-based reduced-order models (pROMs) show great promise as a means to accelerate many-query applications such as forward error propagation, solving inverse problems, and design optimization. In order to deploy pROMs in the context of high-consequence decision making, accurate error estimates are required to determine the region(s) of applicability in the parameter space. The following paper considers the dual-weighted residual (DWR) error estimate for pROMs and compares it to another promising pROM error estimate, machine learned error models (MLEM). Here, we show how DWR can be applied to ROMs and then evaluate DWR on two partial differential equations (PDEs): a two-dimensional linear convection–reaction–diffusion equation, and a three-dimensional static hyper-elastic beam. It is shown that DWR is able to estimate errors for pROMs extrapolating outside of their training set while MLEM is best suited for pROMs used to interpolate within the pROM training set.

42 ENGINEERING↗
DOE OSTI2026

Recent progress on coarse graining simulations

We focus on coarse graining simulations based on the primary conservation equations, effectively codesigned physics and algorithms, and low-Mach-number corrected (LMC) hydrodynamics. Simulation methods involve LANL’s x-Radiation-Adaptive-Grid-Eulerian Large-Eddy Simulation, Besnard-Harlow-Rauenzahn (BHR) Reynolds-Averaged Navier-Stokes (RANS) approach, and Dynamic BHR – a paradigm bridging RANS and LES. A relevant question addressed relates to whether 3D RANS and RANS/LES hybrids – the industry standards for aerospace and automotive research, are presently relevant for practical variable-density applications involving shocked and accelerated interface instabilities. Furthermore, recent simulations of the GaTECH inclined mixing-layer shock-tube and NIF ICF-capsule experiments are used to demonstrate issues, challenges, and potential for 3D coarse grained LMC simulation strategies for robustly simulating complex transitional and coupled hydrodynamics-multiphysics with coarser resolution. Present LES readiness to provide accurate predictions at scale is demonstrated – whereas 3D RANS and RANS/LES bridging do not appear impactful in this context.

42 ENGINEERING↗
DOE OSTI2023

Transferring a Molecular Foundation Model for Polymer Property Predictions

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. Further, state-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

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

Field Programmable Gate Arrays for Enhancing the Speed and Energy Efficiency of Quantum Dynamics Simulations

We present the first application of field programmable gate arrays (FPGAs) as new, customizable hardware architectures for carrying out fast and energy-efficient quantum dynamics simulations of large chemical/material systems. Instead of tailoring the software to fixed hardware, which is the typical case for writing quantum chemistry code for central processing units (CPUs) and graphics processing units (GPUs), FPGAs allow us to directly customize the underlying hardware (even at the level of specific electrical signals in the circuit) to give a truly optimized computational performance for quantum dynamics calculations. By offloading the most intensive and repetitive calculations onto an FPGA, we show that the computational performance of our real-time electron dynamics calculations can even exceed that of optimized commercial mathematical libraries running on high-performance GPUs. In addition to this impressive computational speedup, we show that FPGAs are immensely energy-efficient and consume 4 times less energy than modern GPU or CPU architectures. These energy savings are a practical and important metric for supercomputing centers (many of which exceed over $1 million in power costs alone), as exascale computing capabilities become more widespread and commonplace. Taken together, the implementation techniques and performance metrics of our study demonstrate that FPGAs could play a promising role in upcoming quantum chemistry and materials science applications, particularly for the acceleration and energy-efficient execution of quantum dynamics calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Bayesian Optimization of Catalysis with In-Context Learning

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water–gas shift (RWGS) reaction, BO-ICL identifies multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI.

Calibration↗
DOE OSTI2026

Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport

MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.

MXene↗
DOE OSTI2024

Barcoded overexpression screens in gut Bacteroidales identify genes with roles in carbon utilization and stress resistance

Abstract A mechanistic understanding of host-microbe interactions in the gut microbiome is hindered by poorly annotated bacterial genomes. While functional genomics can generate large gene-to-phenotype datasets to accelerate functional discovery, their applications to study gut anaerobes have been limited. For instance, most gain-of-function screens of gut-derived genes have been performed in Escherichia coli and assayed in a small number of conditions. To address these challenges, we develop Barcoded Overexpression BActerial shotgun library sequencing (Boba-seq). We demonstrate the power of this approach by assaying genes from diverse gut Bacteroidales overexpressed in Bacteroides thetaiotaomicron . From hundreds of experiments, we identify new functions and phenotypes for 29 genes important for carbohydrate metabolism or tolerance to antibiotics or bile salts. Highlights include the discovery of a d -glucosamine kinase, a raffinose transporter, and several routes that increase tolerance to ceftriaxone and bile salts through lipid biosynthesis. This approach can be readily applied to develop screens in other strains and additional phenotypic assays.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Physics of E × B discharges relevant to plasma propulsion and similar technologies

This paper provides perspectives on recent progress in understanding the physics of devices in which the external magnetic field is applied perpendicular to the discharge current. This configuration generates a strong electric field that acts to accelerate ions. The many applications of this set up include generation of thrust for spacecraft propulsion and separation of species in plasma mass separation devices. These “E × B” plasmas are subject to plasma–wall interaction effects and to various micro- and macroinstabilities. In many devices we also observe the emergence of anomalous transport. This perspective presents the current understanding of the physics of these phenomena and state-of-the-art computational results, identifies critical questions, and suggests directions for future research.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗