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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

Sampling from known probability distributions is a ubiquitous task in computational science, underlying calculations in domains from linguistics to biology and physics. Generative machine-learning (ML) models have emerged as a promising tool in this space, building on the success of this approach in applications such as image, text, and audio generation. Often, however, generative tasks in scientific domains have unique structures and features—such as complex symmetries and the requirement of exactness guarantees—that present both challenges and opportunities for ML. This Perspective outlines the advances in ML-based sampling motivated by lattice quantum field theory, in particular for the theory of quantum chromodynamics. Enabling calculations of the structure and interactions of matter from our most fundamental understanding of particle physics, lattice quantum chromodynamics is one of the main consumers of open-science supercomputing worldwide. Here, the design of ML algorithms for this application faces profound challenges, including the necessity of scaling custom ML architectures to the largest supercomputers, but also promises immense benefits, and is spurring a wave of development in ML-based sampling more broadly. In lattice field theory, if this approach can realize its early promise it will be a transformative step towards first-principles physics calculations in particle, nuclear and condensed matter physics that are intractable with traditional approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Saturation physics of threshold heat-flux reduction

The saturation physics of ion-temperature-gradient-driven turbulence is examined in relation to the temperature-gradient variation of the heat flux, which can exhibit an upshift of the critical gradient for significant flux relative to the linear instability threshold. Gyrokinetic measurements of saturation properties and spectral energy transfer, which will be defined in Sec. II, are presented, indicating that the physics of saturation is fundamentally unchanged on either side of the upshifted gradient. To analyze heat transport below and above the upshifted critical gradient, a fluid model for toroidal ion-temperature-gradient turbulence is modified to include the kinetic instability threshold. The model and the heat flux are rendered in the eigenmode decomposition to track the dominant mode-coupling channel of zonal-flow-catalyzed transfer to a conjugate stable mode. Given linear and nonlinear symmetries, the stable mode level and the cross-correlation of the unstable and stable mode amplitudes are related to the unstable mode level via linear physics. Here, the heat flux can then be written in terms of the unstable-mode level, which through a nonlinear balance depends on the eigenmode-dependent coupling coefficients and the triplet correlation time of the dominant coupled modes. Resonance in these quantities leads to suppressed heat flux above the linear threshold, with a nonlinear upshift of the critical gradient set by the resonance broadening of a finite perpendicular wavenumber and collisionality.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dark matter, dark energy and fundamental acceleration

We discuss the existence of an acceleration scale in galaxies and galaxy clusters and its relevance for the nature of dark matter. The presence of the same acceleration scale found at very different length scales, and in very different astrophysical objects, strongly supports the existence of a fundamental acceleration scale governing the observed gravitational physics. We comment on the implications of such a fundamental acceleration scale for constraining cold dark matter models as well as its relevance for structure formation to be explored in future numerical simulations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Scaling Containment on a Large Centrifuge

Containment science concerns the trapping or leakage of radioactive cavity gases from underground nuclear explosions (UNEs). The most physically relevant data for validation of containment science comes from legacy UNE testing or large field-scale chemical explosions. Field-scale tests can inform on the degree of residual stress imparted to host geology and possible formation of an accompanying gas-containing “stress cage”, for example, as a function of chemical explosion yield. However, field testing can be expensive, difficult to conduct, and challenging to instrument with limitations on data coverage (i.e., the number of boreholes; difficult coring through damage zones). Small-scale laboratory tests are typically simpler to conduct and more thoroughly characterize, but may not include the right scale of containment processes for stress-cage or chimney formation. This report presents the theory and fundamentals of large centrifuge physical modeling, including a history of explosive or similar testing relevant to containment science investigations on centrifuges. Subscale models with embedded chemical explosives in the enhanced gravity of a large centrifuge can represent hundreds of meters of depth and large explosions not otherwise attainable in the laboratory as based on scaling of length, energy, and other processes by the g-factor of the centrifuge (i.e., the number of times larger the centrifugal force is than the gravitational force on the Earth’s surface). The centrifuge uniquely joins the simplicity and exhaustiveness of laboratory-scale characterization with the physics of field-scale processes. This report is part of Sandia National Laboratories’ (SNL’s) effort to prepare for physical modeling with approximately one-meter tall geologic models of stress cage and/or chimney formation on its 29-foot [8.84 m] radius centrifuge with a load capacity of 1.6 million 𝑔-pounds [726 𝑔-ton]. The scaling relationships herein will inform model design decisions and performance requirements for in-flight sensors.

42 ENGINEERING↗
DOE OSTI2022

Physical and Mechano-Electrochemical Phenomena of Thin Film Lithium-Ceramic Electrolyte Constructs

While a small number of solid electrolytes exhibit high ionic conductivity (~1mS cm at 298K), few are stable against Li metal. The garnet-type solid electrolyte, based on the nominal formula Li 7 La 3 Zr 2 O 12 (LLZO), is unique in that it is a fast ion conductor and – as we demonstrated in our recent project (DE-EE-00006821) – is stable against Li. Moreover, our former project successfully demonstrated a decrease in Li-LLZO interface resistance from 12,000 to 2 Ohms cm 2 and stable cycling at 1 mA cm 2 for 100 cycles (+/- 15 µm Li per cycle). Although the past project (DE-EE-00006821) demonstrated LLZO is a viable solid electrolyte for enabling batteries using metallic Li, the studies used thick pellets (1mm) and thick anodes (~500 µm). We believe that to achieve a step increase in technology readiness level (TRL), the same performance characteristics previously shown should be demonstrated in technologically relevant cells, e.g. thin LLZO and thin Li. The goal of this project is to acquire a deep fundamental understanding of the physical and mechano-electrochemical phenomena that control the performance of cells consisting of thin LLZO (~10 µm), thin Li anodes (~20 µm) and thin solid-state composite cathodes. If successful, the knowledge gained in this project will guide closely related commercialization efforts to scale the production of LLZO-based solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Strange Mesons and Gluonic Excitations

In the standard model of particle physics, the interactions between the fundamental constituents of nuclear matter, quarks and gluons, are governed by the theory of Quantum Chromodynamics (QCD). A central goal of nuclear physics is to understand how hadrons, such as protons and neutrons, are formed from these underlying quark and gluon degrees of freedom. A hadron is primarily constructed from three quarks or a quark-antiquark pair; however, the theory of QCD allows for much more exotic configurations. One of the predicted exotic configurations is known as a hybrid meson, which contains an excited gluonic field in addition to the usual quark-antiquark pair. This project aims to search for and study these gluonic excitations using the Gluonic Excitation (GlueX) experiment at Jefferson Lab in Newport News, VA. The discovery potential of the experiment will be significantly extended by studying the quark flavor composition of the meson spectrum through the completion and use of an enhanced detector to identify mesons containing strange quarks. The unprecedented statistical precision of the data collected at GlueX will allow us to search for a pattern of light-quark hybrid mesons, providing new insight into the interactions that bind the fundamental quarks and gluons into the hadrons we observe in nature.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Confronting interacting dark radiation scenarios with cosmological data

Dark radiation (DR) is generally predicted in new physics scenarios that address fundamental puzzles of the Standard Model or tensions in the cosmological data. Cosmological data have the sensitivity to constrain not only the energy density of DR but also whether it is interacting. In this paper, we present a systematic study of five types of interacting DR (free-streaming, fluid, decoupling, instantaneous decoupling, and recoupling DR) and their impact on cosmological observables. We modify the Boltzmann hierarchy to describe all these types of interacting DR under the relaxation time approximation. We, for the first time, robustly calculate the collision terms for recoupling scalar DR and provide a better estimation of the recoupling transition redshift. We demonstrate the distinct features of each type of DR on the cosmic microwave background and matter power spectra. We perform Markov-chain Monte Carlo scans using the Planck 2018 data and baryon acoustic oscillation data. Assuming no new physics in the standard model neutrino sector, we find no statistically significant constraints on the couplings of DR, although there is a slight preference for the fluidlike limit of all the cases. In the case of instantaneous decoupling DR, this limit corresponds to a late transition redshift around recombination. Furthermore, the ΔN eff constraint varies marginally depending on the type of DR.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Is stochastic thermodynamics the key to understanding the energy costs of computation?

The relationship between the thermodynamic and computational properties of physical systems has been a major theoretical interest since at least the 19th century. It has also become of increasing practical importance over the last half-century as the energetic cost of digital devices has exploded. Importantly, real-world computers obey multiple physical constraints on how they work, which affects their thermodynamic properties. Moreover, many of these constraints apply to both naturally occurring computers, like brains or Eukaryotic cells, and digital systems. Most obviously, all such systems must finish their computation quickly, using as few degrees of freedom as possible. This means that they operate far from thermal equilibrium. Furthermore, many computers, both digital and biological, are modular, hierarchical systems with strong constraints on the connectivity among their subsystems. Yet another example is that to simplify their design, digital computers are required to be periodic processes governed by a global clock. None of these constraints were considered in 20th-century analyses of the thermodynamics of computation. The new field of stochastic thermodynamics provides formal tools for analyzing systems subject to all of these constraints. We argue here that these tools may help us understand at a far deeper level just how the fundamental thermodynamic properties of physical systems are related to the computation they perform.

computation↗
DOE OSTI2023

Scaling transformations and the origins of light relics constraints from cosmic microwave background observations

Here, we use here a family of scaling transformations, that scale key rates in the evolution equations, to analytically understand constraints on light relics from cosmic microwave background (CMB) maps, given cosmological models of varying degrees of complexity. We describe the causes of physical effects that are fundamentally important to the constraining power of the data, with a focus on the two that are most novel. We use as a reference model a cosmological model that admits a scaling transformation that increases light relic energy density while avoiding all of these causes. Constraints on light relics in a given model can then be understood as due to the differences between the given model and the reference model, as long as the additional light relics only interact gravitationally with the Standard Model components. This understanding supports the development of cosmological models that can evade light relics constraints from CMB maps.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2023

IRIS-HEP Strategic Plan for the Next Phase of Software Upgrades for HL-LHC Physics

The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors. CERN's Large Hadron Collider (LHC) represents a huge step forward in this quest. The discovery of the Higgs boson, the observation of exceedingly rare decays of $B$ mesons, and stringent constraints on many viable theories of physics beyond the Standard Model (SM) demonstrate the great scientific value of the LHC physics program. The next phase of this global scientific project will be the High-Luminosity LHC (HL-LHC) which will collect data starting circa 2029 and continue through the 2030s. The primary science goal is to search for physics beyond the SM and, should it be discovered, to study its implications. In the HL-LHC era, the ATLAS and CMS experiments will record around 100 times as many collisions as were used to discover the Higgs boson (and at twice the energy). Both NSF and DOE are making large detector upgrade investments so the HL-LHC can operate in this high-rate environment. Similar investment in software R&D for acquiring, managing, processing and analyzing HL-LHC data is critical to maximize the return-on-investment in the upgraded accelerator and detectors. This report presents a strategic plan for a possible second 5-year funded phase (2023 through 2028) for the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) which will close remaining software and computing gaps to deliver HL-LHC science.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery

The standard model of particle physics describes the known fundamental particles and forces that make up our Universe, with the exception of gravity. One of the central features of the standard model is a field that permeates all of space and interacts with fundamental particles. The quantum excitation of this field, known as the Higgs field, manifests itself as the Higgs boson, the only fundamental particle with no spin. In 2012, a particle with properties consistent with the Higgs boson of the standard model was observed by the ATLAS and CMS experiments at the Large Hadron Collider at CERN. Since then, more than 30 times as many Higgs bosons have been recorded by the ATLAS experiment, enabling much more precise measurements and new tests of the theory. Here, on the basis of this larger dataset, we combine an unprecedented number of production and decay processes of the Higgs boson to scrutinize its interactions with elementary particles. Interactions with gluons, photons, and W and Z bosons—the carriers of the strong, electromagnetic and weak forces—are studied in detail. Interactions with three third-generation matter particles (bottom (b) and top (t) quarks, and tau leptons (τ)) are well measured and indications of interactions with a second-generation particle (muons, μ) are emerging. These tests reveal that the Higgs boson discovered ten years ago is remarkably consistent with the predictions of the theory and provide stringent constraints on many models of new phenomena beyond the standard model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Synthesis, Processing, and Use of Isotopically Enriched Epitaxial Oxide Thin Films

Isotopic engineering has emerged as a key approach to study the nucleation, diffusion, phase transitions, and reactions of materials at an atomic level. It aims to uncover mass transport pathways, kinetics, and operational and failure mechanisms of functional materials and devices. Understanding these phenomena leads to deeper insights into important physical processes, such as the transport of ions in energy conversion and storage devices and the role of active sites and supports during heterogeneous catalytic reactions. Likewise, isotopic engineering is being pursued as a means of modifying functionality to enable future technological applications. In this Account, we summarize our recent work employing isotope labeling (e.g., 18 O 2 and 57 Fe) during thin film synthesis and postgrowth processing to reveal growth mechanisms, defect chemistry, and elemental diffusion under working and extreme conditions. Isotope-resolved analysis techniques with nanometer-scale spatial resolution, such as time-of-flight secondary ion mass spectrometry and atom probe tomography, facilitate the accurate quantification of isotopic placement and concentration in our well-defined heterostructures with precisely positioned, isotope-enriched layers. By measuring the nanometer-scale redistribution between natural abundance and isotopically enriched oxygen layers during the deposition of Fe 2 O 3 and Cr 2 O 3 by molecular beam epitaxy, we identified intermixing processes driven by surface adatoms occurring both at the film growth surface and within the first few layers below the surface. Further insights into synthesis mechanisms were gained by studying the tungsten oxide thin films grown by evaporating WO 3 powder in the presence of background 18 O 2 , revealing minimal incorporation of background oxygen during the film formation process. Thermal and radiation-enhanced diffusion in epitaxial Fe and Cr oxides were precisely tracked using 18 O and 57 Fe tracer layers incorporated into model epitaxial oxide thin films. This approach has allowed us to access thermal diffusion behavior at lower temperatures than previously measured, revealing a potential changeover in diffusion mechanism. Understanding radiation-enhanced diffusion in model oxides that represent the surface layers on the structural components of nuclear reactors informs our understanding of their corrosion behavior under irradiation. Isotopic labeling can also provide unique insights into the surface exchange reactions and defect chemistry of electrocatalysts. For instance, tracking the change in 18 O concentration at the surface of an epitaxial LaNiO 3 thin film after the electrocatalytic oxygen evolution reaction revealed the participation of lattice oxygen, confirming a hypothesis that had been proposed previously. Lastly, we highlight a new direction wherein we perform in situ processing studies utilizing isotopic tracers in conjunction with model epitaxial thin films within the atom probe tomography instrument. Additionally, this Account illustrates the great potential of isotopic engineering to enable fundamental mechanistic insights into physical processes and engineer functional properties in epitaxial films, heterostructures, and superlattices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating the p--π ± and p--p--π ± dynamics with femtoscopy in pp collisions at $\sqrt{s}$ = 13 TeV

The interaction between pions and nucleons plays a crucial role in hadron physics. It represents a fundamental building block of the low-energy QCD dynamics and is subject to several resonance excitations. This work studies the p--π ± dynamics using femtoscopic correlations in high-multiplicity pp collisions at $\sqrt{s}$ = 13 TeV measured by ALICE at the LHC. As the final-state interaction between protons and pions is well constrained by scattering experiments and the study of pionic hydrogen, the results give access to information on the particle-emitting source in pp collisions using the femtoscopy methods. The scaling of the source size of primordial protons and pions against their pair transverse mass is extracted. The results are compared with the source sizes studied with p–p, p--K + , and π ± –π ± pairs by ALICE in the same collision system and are found to be in agreement for the different particle pairs. This reinforces recent findings by ALICE of a common emission source for all hadron-pairs in pp collisions at LHC energies. Furthermore, the p--p--π ± systems are studied using three-particle femtoscopy in pp collisions at TeV. The presence of three-body effects is analyzed utilizing the cumulant expansion method. In this formalism, the known two-body interactions are subtracted in order to isolate the three-body effects. For both, p--p--π + and p--p--π – , a non-zero cumulant is found, indicating effects beyond pairwise interactions. These results give information on the coupling of the pion to multiple nucleons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Progress in Epitaxial Thin-Film Na 3 Bi as a Topological Electronic Material

Trisodium bismuthide (Na 3 Bi) is the first experimentally verified topological Dirac semimetal, and is a 3D analogue of graphene hosting relativistic Dirac fermions. Its unconventional momentum-energy relationship is interesting from a fundamental perspective, yielding exciting physical properties such as chiral charge carriers, the chiral anomaly, and weak anti-localization. It also shows promise for realizing topological electronic devices such as topological transistors. Herein, an overview of the substantial progress achieved in the last few years on Na 3 Bi is presented, with a focus on technologically relevant large-area thin films synthesized via molecular beam epitaxy. Key theoretical aspects underpinning the unique electronic properties of Na 3 Bi are introduced. Next, the growth process on different substrates is reviewed. Spectroscopic and microscopic features are illustrated, and an analysis of semiclassical and quantum transport phenomena in different doping regimes is provided. Furthermore, the emergent properties arising from confinement in two dimensions, including thickness-dependent and electric-field-driven topological phase transitions, are addressed, with an outlook toward current challenges and expected future progress.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Transient Nanoscopy of Exciton Dynamics in 2D Transition Metal Dichalcogenides

Abstract The electronic and optical properties of 2D transition metal dichalcogenides are dominated by strong excitonic resonances. Exciton dynamics plays a critical role in the functionality and performance of many miniaturized 2D optoelectronic devices; however, the measurement of nanoscale excitonic behaviors remains challenging. Here, a near‐field transient nanoscopy is reported to probe exciton dynamics beyond the diffraction limit. Exciton recombination and exciton–exciton annihilation processes in monolayer and bilayer MoS 2 are studied as the proof‐of‐concept demonstration. Moreover, with the capability to access local sites, intriguing exciton dynamics near the monolayer‐bilayer interface and at the MoS 2 nano‐wrinkles are resolved. Such nanoscale resolution highlights the potential of this transient nanoscopy for fundamental investigation of exciton physics and further optimization of functional devices.

2D materials↗
DOE OSTI2026

Generative Models for Crystalline Materials

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material spaces to identify promising candidates for various applications. More recently, research efforts have increasingly shifted toward generating crystal structures using end-to-end generative models. This review analyzes the current state of generative modeling for crystal structure prediction and de novo generation. It examines crystal representations, outlines the generative models used to design crystal structures, and evaluates their respective strengths and limitations. Furthermore, the review highlights experimental considerations for evaluating generated structures and provides recommendations for suitable existing software tools. Emerging topics, such as modeling disorder and defects, integration in advanced characterization, incorporating synthetic feasibility constraints, and model explainability are explored. Ultimately, this work aims to inform both experimental scientists looking to adapt suitable ML models to their specific circumstances and ML specialists seeking to understand the unique challenges related to inverse materials design and discovery.

Metni, Houssam [Karlsruhe Inst. of Technology (KIT↗