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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Future Collider Options for the US

The United States has a rich history in high energy particle accelerators and colliders -- both lepton and hadron machines, which have enabled several major discoveries in elementary particle physics. To ensure continued progress in the field, U.S. leadership as a key partner in building next generation collider facilities abroad is essential; also critically important is the exploring of options to host a future collider in the U.S. The "Snowmass" study and the subsequent Particle Physics Project Prioritization Panel (P5) process provide the timely opportunity to develop strategies for both. What we do now will shape the future of our field and whether the U.S. will remain a world leader in these areas. In this white paper, we briefly discuss the US engagement in proposed collider projects abroad and describe future collider options for the U.S. We also call for initiating an integrated R&D program for future colliders.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Scaling SQL to the Supercomputer for Interactive Analysis of Simulation Data

AI and simulation workloads consume and generate large amounts of data that need to be searched, transformed and merged with other data. With the goal of treating data as a first-class citizen inside a traditionally compute-centric HPC environment, we explore how the use of accelerators and high-speed interconnects can speed up tasks which otherwise constitute bottlenecks in computational discovery workflows. BlazingSQL is SQL engine that runs natively on NVIDIA GPUs and supports internode communication for fast analytics on terabyte-scale tabular data sets. We show how a fast interconnect improves query performance if leveraged through the Unified Communication X (UCX) middleware. We envision that future computing platforms will integrate accelerated database query capabilities for immediate and interactive analysis of large simulation data.

Glaser, Jens↗
DOE OSTI2023

Future high energy colliders and options for the U.S.

The United States has a rich history in high energy particle accelerators and colliders — both lepton and hadron machines, which have enabled several major discoveries in elementary particle physics. To ensure continued progress in the field, U.S. leadership as a key partner in building next generation collider facilities abroad is essential; also critically important is to prepare to host an energy frontier collider in the U.S. once the construction of the LBNF/DUNE project is completed. In this paper, we briefly discuss the ongoing and potential U.S. engagement in proposed collider projects abroad and present a number of future collider options we have studied for hosting an energy frontier collider in the U.S. Further, we also call for initiating an integrated national R&D program in the U.S. now, focused on future colliders.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Destabilizing high-capacity high entropy hydrides via earth abundant substitutions: From predictions to experimental validation

The vast chemical space of high entropy alloys (HEAs) makes trial-and-error experimental approaches for materials discovery intractable and often necessitates data-driven and/or first principles computational insights to successfully target materials with desired properties. In the context of materials discovery for hydrogen storage applications, a theoretical prediction-experimental validation approach can vastly accelerate the search for substitution strategies to destabilize high-capacity hydrides based on benchmark HEAs, e.g. TiVNbCr alloys. Here, in this study, machine learning predictions, corroborated by density functional theory calculations, predict substantial hydride destabilization with increasing substitution of earth-abundant Fe content in the (TiVNb) 75 Cr 25-x Fe x system. The as-prepared alloys crystallize in a single-phase bcc lattice for limited Fe content x < 7, while larger Fe content favors the formation of a secondary C14 Laves phase intermetallic. Short range order for alloys with x < 7 can be well described by a random distribution of atoms within the bcc lattice without lattice distortion. Hydrogen absorption experiments performed on selected alloys validate the predicted thermodynamic destabilization of the corresponding fcc hydrides and demonstrate promising lifecycle performance through reversible absorption/desorption. This demonstrates the potential of computationally expedited hydride discovery and points to further opportunities for optimizing bcc alloy ↔ fcc hydrides for practical hydrogen storage applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Mechanochemical Solid Form Screening of Zeolitic Imidazolate Frameworks Using Structure-Directing Liquid Additives

We demonstrate a systematic application of the mechanochemical liquid-assisted grinding (LAG) methodology to screen for forms of zinc imidazolate (ZnIm 2 ), of fundamental importance as the simplest member of the zeolitic imidazolate framework materials family. The exploration of 45 different liquid additives, selected based on their molecular structure and physicochemical properties has resulted in eight different ZnIm 2 topological forms, appearing in 13 crystallographically distinct solid forms (including two previously unknown forms of the crb (BCT) topology), amorphous phases, and the interrupted moc-Zn 4 Im 8 HIm. All prepared topological forms were also explored computationally, using dispersion-corrected periodic density functional theory (DFT) calculations, enabling the rationalization of screening outcomes, and setting the stage for future prediction of additive-directed metal–organic framework (MOF) synthesis. This first systematic exploration of LAG in screening for three-dimensional MOFs demonstrates the potential of the liquid additive to not only accelerate materials synthesis, but also to direct it toward topologically different MOFs. The discovery of novel forms of a material that already exhibits at least 21 crystallographically and functionally different forms provides a strong testimony on the power of mechanochemistry in metal–organic materials discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Combined experimental-theoretical study of electron mobility-limiting mechanisms in SrSnO 3

The discovery and development of ultra-wide bandgap (UWBG) semiconductors is crucial to accelerate the adoption of renewable power sources. This necessitates an UWBG semiconductor that exhibits robust doping with high carrier mobility over a wide range of carrier concentrations. Here we demonstrate that epitaxial thin films of the perovskite oxide Nd x Sr 1 – x SnO 3 (SSO) do exactly this. Nd is used as a donor to successfully modulate the carrier concentration over nearly two orders of magnitude, from 3.7 × 10 18 cm –3 to 2.0 × 10 20 cm –3 . Despite being grown on lattice-mismatched substrates and thus having relatively high structural disorder, SSO films exhibited the highest room-temperature mobility, ~70 cm 2 V –1 s –1 , among all known UWBG semiconductors in the range of carrier concentrations studied. The phonon-limited mobility is calculated from first principles and supplemented with a model to treat ionized impurity and Kondo scattering. This produces excellent agreement with experiment over a wide range of temperatures and carrier concentrations, and predicts the room-temperature phonon-limited mobility to be 76–99 cm 2 V –1 s –1 depending on carrier concentration. This work establishes a perovskite oxide as an emerging UWBG semiconductor candidate with potential for applications in power electronics

36 MATERIALS SCIENCE↗
DOE OSTI2023

Highly transferable atomistic machine-learning potentials from curated and compact datasets across the periodic table

Machine learning atomistic potentials trained using density functional theory (DFT) datasets allow for the modeling of complex material properties with near-DFT accuracy while imposing a fraction of its computational cost. The curation of the DFT datasets can be extensive in size and time-consuming to train and refine. In this study, we focus on addressing these barriers by developing minimalistic and flexible datasets for many elements in the periodic table regardless of their mass, electronic configuration, and ground state lattice. These DFT datasets have on average, ~4000 different structures and 27 atoms per structure, which we found sufficient to maintain the predictive accuracy of DFT properties and notably with high transferability. We envision these highly curated training sets as starting points for the community to expand, modify, or use with other machine learning atomistic potential models, whatever may suit individual needs, further accelerating the utilization of machine learning as a tool for material design and discovery.

42 ENGINEERING↗
DOE OSTI2020

An ultra-compact x-ray free-electron laser

In the field of beam physics, two frontier topics have taken center stage due to their potential to enable new approaches to discovery in a wide swath of science. These areas are: advanced, high gradient acceleration techniques, and x-ray free electron lasers (XFELs). Further, there is intense interest in the marriage of these two fields, with the goal of producing a very compact XFEL. In this context, recent advances in high gradient radio-frequency cryogenic copper structure research have opened the door to the use of surface electric fields between 250 and 500 MV m -1 . Such an approach is foreseen to enable a new generation of photoinjectors with six-dimensional beam brightness beyond the current state-of-the-art by well over an order of magnitude. This advance is an essential ingredient enabling an ultra-compact XFEL (UC-XFEL). In addition, one may accelerate these bright beams to GeV scale in less than 10 m. Such an injector, when combined with inverse free electron laser-based bunching techniques can produce multi-kA beams with unprecedented beam quality, quantified by 50 nm-rad normalized emittances. The emittance, we note, is the effective area in transverse phase space (x, p x /m e c) or (y, p y /m e c) occupied by the beam distribution, and it is relevant to achievable beam sizes as well as setting a limit on FEL wavelength. These beams, when injected into innovative, short-period (1–10 mm) undulators uniquely enable UC-XFELs having footprints consistent with university-scale laboratories. We describe the architecture and predicted performance of this novel light source, which promises photon production per pulse of a few percent of existing XFEL sources. We review implementation issues including collective beam effects, compact x-ray optics systems, and other relevant technical challenges. To illustrate the potential of such a light source to fundamentally change the current paradigm of XFELs with their limited access, we examine possible applications in biology, chemistry, materials, atomic physics, industry, and medicine—including the imaging of virus particles—which may profit from this new model of performing XFEL science.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
DOE OSTI2024

Improving the Discovery Power of the Dark Energy Survey Gravitational Wave Program in Its Fourth Observing Campaign

The universe is growing at an accelerated rate, yet the several experiments and science cases used to measure this property yield conflicting results for the rate. Known colloquially as the Hubble tension problem, this discrepancy calls for a novel measurement of the Hubble Constant, H0, in order to be resolved. The Dark Energy Survey Gravitational Wave group (DESGW) aims to relieve this tension by performing a standard sirens measurement of H0 independent of traditional methods. To do so in the current era of data abundance and limited telescope time demands a robust, optimized, and complete science software pipeline to carefully balance the transition from gravitational wave data to state-of-the-art images, images to photometric data, and photometric data to identification and analysis of electromagnetic counterparts to gravitational wave signals. This thesis details the contributions of the author to the DESGW Search & Discovery Pipeline for the fourth observing run of the LIGO/Virgo/KAGRA gravitational wave detector network, as well as the science applications that have been pursued.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Enabling discovery data science through cross-facility workflows

Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.

Antypas, Katerina B.↗
DOE OSTI2022

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based models, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward ( a) the discovery of new materials through large-scale enumerative screening, ( b) the design of materials through identification of rules and principles that govern materials properties, and ( c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design.

Chemistry↗
DOE OSTI2022

Irradiation Facilities and Irradiation Methods for High Power Target

High Power Target systems are key elements in future neutrino and other rare particle production in accelerators. These systems transform an intense source of protons into secondary particles of interest to enable new scientific discoveries. As beam intensity and energies increase, target systems face significant challenges. Radiation damages and thermal shocks in target materials were identified as the leading cross-cutting challenges of high-power target facilities. Target material R&D to address these challenges are essential to enable and ensure reliable operation of future-generation accelerators. Irradiation facilities and alternative methods are critical to provide a full support of material R&D and better address these critical challenges.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Modeling laser-driven ion acceleration with deep learning

Developments in machine learning promise to ameliorate some of the challenges of modeling complex physical systems through neural-network-based surrogate models. High-intensity, short-pulse lasers can be used to accelerate ions to mega-electronvolt energies, but to model such interactions requires computationally expensive techniques such as particle-in-cell simulations. Multilayer neural networks allow one to take a relatively sparse ensemble of simulations and generate a surrogate model that can be used to rapidly search the parameter space of interest. In this study, we created an ensemble of over 1,000 simulations modeling laser-driven ion acceleration and developed a surrogate to study the resulting parameter space. A neural-network-based approach allows for rapid feature discovery not possible for traditional parameter scans given the computational cost. A notable observation made during this study was the dependence of ion energy on the pre-plasma gradient length scale. While this methodology harbors great promise for ion acceleration, it has ready application to all topics in which large-scale parameter scans are restricted by significant computational cost or relatively large, but sparse, domains.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Detecting Thermally-Induced Spinodal Decomposition with Picosecond Ultrasonics in Cast Austenitic Stainless Steels

Destructive techniques to monitor nuclear reactor component health may not always be available during service, as they are time-consuming and often require pre-installed inspection coupons. Non-destructive evaluation (NDE) techniques can bridge this gap by rapidly identifying the state of mission-critical reactor components, via inference between NDE-measurable material properties and those of ultimate interest, such as ductility and toughness. Here, we demonstrate one such inference about the health of thermally aged cast austenitic stainless steels. Observations of surface acoustic wave peak (SAW) splitting correlate with spinodal decomposition-induced embrittlement as destructively measured by Charpy impact energy. Elastodynamic calculations and molecular dynamics simulations of the effects of spinodal decomposition on elastic moduli support that the new acoustic modes present are due to stiffening in the δ-ferrite domains. Finally, this discovery enables one to probe structure-property relationships in materials in a greatly accelerated manner, suggesting that similar inference methods can be used to determine material fitness-for-service, or to quickly uncover new structure-property relationships.

304-type stainless steel↗
DOE OSTI2024

A Cellular Automaton Simulation for Predicting Phase Evolution in Solid-State Reactions

New computational tools for solid-state synthesis recipe design are needed in order to accelerate the experimental realization of novel functional materials proposed by high-throughput materials discovery workflows. This work contributes a cellular automaton simulation framework for predicting the time-dependent evolution of intermediate and product phases during solid-state reactions as a function of precursor choice and amount, reaction atmosphere, and heating profile. The simulation captures the effects of reactant particle spatial distribution, particle melting, and reaction atmosphere. Reaction rates based on rudimentary kinetics are estimated using density functional theory data from the Materials Project and machine learning estimators for the melting point and the vibrational entropy component of the Gibbs free energy. The resulting simulation framework allows for the prediction of the likely outcome of a reaction recipe before any experiments are performed. We analyze five experimental solid-state recipes for BaTiO 3 , CaZrN 2 , and YMnO 3 found in the literature to illustrate the performance of the model in capturing reaction selectivity and reaction pathways as a function of temperature and precursor choice. This simulation framework offers an easier way to optimize existing recipes, aid in the identification of intermediates, and design effective recipes for yet unrealized inorganic solids in silico .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

IceCube-Gen2: the window to the extreme Universe

The observation of electromagnetic radiation from radio to γ-ray wavelengths has provided a wealth of information about the Universe. However, at PeV (10 15 eV) energies and above, most of the Universe is impenetrable to photons. New messengers, namely cosmic neutrinos, are needed to explore the most extreme environments of the Universe where black holes, neutron stars, and stellar explosions transform gravitational energy into non-thermal cosmic rays. These energetic particles havemillions of times higher energies than those produced in the most powerful particle accelerators on Earth. As neutrinos can escape from regions otherwise opaque to radiation, they allow an unique view deep into exploding stars and the vicinity of the event horizons of black holes. The discovery of cosmic neutrinos with IceCube has opened this new window on the Universe. IceCube has been successful in finding first evidence for cosmic particle acceleration in the jet of an active galactic nucleus. Yet, ultimately, its sensitivity is too limited to detect even the brightest neutrino sources with high significance, or to detect populations of less luminous sources. In thiswhite paper, we present an overview of a next-generation instrument, IceCube-Gen2, which will sharpen our understanding of the processes and environments that govern the Universe at the highest energies. IceCube-Gen2 is designed to: (a) Resolve the high-energy neutrino sky from TeV to EeV energies (b) Investigate cosmic particle acceleration through multi-messenger observations (c) Reveal the sources and propagation of the highest energy particles in the Universe (d) Probe fundamental physics with high-energy neutrinos IceCube-Gen2 will enhance the existing IceCube detector at the South Pole. It will increase the annual rate of observed cosmic neutrinos by a factor of ten compared to IceCube, and will be able to detect sources five times fainter than its predecessor. Furthermore, through the addition of a radio array, IceCube- Gen2 will extend the energy range by several orders of magnitude compared to IceCube. Construction will take 8 years and cost about $350M. The goal is to have IceCube-Gen2 fully operational by 2033. IceCube-Gen2 will play an essential role in shaping the new era of multimessenger astronomy, fundamentally advancing our knowledge of the highenergy Universe. Overall, this challenging mission can be fully addressed only through the combination of the information from the neutrino, electromagnetic, and gravitational wave emission of high-energy sources, in concert with the new survey instruments across the electromagnetic spectrum and gravitational wave detectors which will be available in the coming years.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗