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Results for “accelerated discovery”

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 757 records · Page 42

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NASA NTRS2018

Preliminary Saturn Atmospheric Density Results From Cassini's Final Plunge

The Cassini spacecraft made its ﬁnal descent into the planet Saturn on September 15, 2017, capping a twenty year mission full of scientiﬁc discoveries. The high gain antenna was held on Earth-point until torques from atmospheric drag caused the spacecraft to lose line-of-sight lock with Earth. The Doppler data collected during the ﬁnal plunge contains information about the spacecraft’s acceleration due to atmospheric drag, and therefore, the density of Saturn’s atmosphere. In this work, we present preliminary analysis of the end of mission Doppler data and its implications regarding the density of Saturn’s upper atmosphere. Are construction of the spacecraft’s ﬁnal trajectory is discussed and used to ﬁt a model of Saturn’s atmosphere to the Doppler data taken during the ﬁnal plunge. The Cassini navigation team’s experience ﬂying the spacecraft through the ﬁnal ﬁve low altitude Saturn periapses is also discussed in the context of atmospheric drag and density models.

Roth, Duane↗

DOE OSTI2024

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Hot Hydride Superconductivity Above 550 K

The search for room temperature superconductivity has accelerated in the last few years driven by experimentally accessible theoretical predictions that indicated alloying dense hydrogen with other elements could produce conventional superconductivity at high temperatures and pressures. These predictions helped inform the synthesis of simple binary hydrides that culminated in the discovery of the superhydride LaH 10 with a superconducting transition temperature T c of 260 K at 180 GPa. We have now successfully synthesized a metallic La-based superhydride with an initial T c of 294 K. When subjected to subsequent thermal excursions that promoted a chemical reaction to a higher order system, the Tc onset was driven irreversibly to 556 K. X-ray characterization confirmed the formation of a distorted LaH 10 based backbone that suggests the formation of ternary or quaternary compounds with substitution at the La and/or H sites. The results provide evidence for hot superconductivity, aligning with recent predictions for higher order hydrides under pressure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1974

Energetic particles from the sun

Discussion of solar cosmic ray phenomena and related topics from the solar physical point of view. Since solar cosmic rays are usually produced by solar flares, it is necessary to understand the processes and mechanism of solar flares, especially the so-called proton flares, in order to understand the acceleration mechanism of solar cosmic rays and their behavior in both the solar atmosphere and interplanetary space. For this reason, a detailed discussion is given of various phenomena associated with solar flares, proton flare characteristics, and the mechanism of solar flares. Since the discovery of solar cosmic rays by Forbush, the interplanetary space has been thought of as a medium in which solar cosmic rays propagate. The propagation of solar cosmic rays in this space is therefore discussed briefly by referring to the observed magnetic properties of this space. Finally, some problems related to the physics of galactic cosmic rays are discussed.

Sakurai, K.↗

NASA NTRS1995

Expanding our knowledge of the heliosphere

Milestones on our road to understanding the heliosphere between 1950 and 1988 are recalled. Among these are early studies of solar energetic particles suggesting a heliospheric boundary at 5 AU, the discovery of the solar wind and the sectored nature of the interplanetary magnetic field. Recent results, particularly from the Ulysses spacecraft, confirm the arrival of neutrals from interstellar space, the pick-up of singly charged ions by the solar wind and the acceleration of these ions to become anomalous cosmic rays. Two distinct solar wind regimes have been discovered. At low heliolatitudes a highly variable solar wind blows at an average speed around 450 km/s, while at high latitudes a relatively smooth 750 km/s flow is observed. No indicators of a dipole-like magnetic field have been seen by Ulysses in solar polar latitudes. The cosmic radiation increase with latitude is much smaller than predicted. The status of and plans for the Voyager 1 and 2, Pioneer 10 and 11, and Ulysses spacecraft are outlined.

Page, D. E.↗

DOE OSTI2021

BETO 2021 Peer Review - Overview of the Chemical Catalysis for Bioenergy Consortium

Catalysis plays a central role in converting biomass and carbon-rich waste feedstocks into fuels and chemicals; however, critical catalysis challenges exist that are limiting commercialization of emerging bioenergy technologies. By leveraging unique U.S. Department of Energy National Laboratory capabilities and expertise, the Chemical Catalysis for Bioenergy consortium seeks to overcome these catalysis challenges and accelerate the catalyst and process development cycle. The foundation of the consortium consists of an integrated and collaborative portfolio of catalytic technologies and enabling capabilities, which positions ChemCatBio to address both technology-specific and overarching catalysis challenges across the development cycle from discovery to scale-up. The core catalysis projects target technological advancements for specific conversion processes, such as catalytic upgrading of biochemical process intermediates, catalytic fast pyrolysis, C1 and C2 upgrading, and electrochemical CO2 reduction, while the enabling technologies provide access to world-class capabilities and expertise in computational modeling, materials synthesis, advanced in situ and in operando catalyst characterization, and catalyst design tools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Alexeev, Yuri↗

DOE OSTI2020

TeraChem: A graphical processing unit-accelerated electronic structure package for large-scale ab initio molecular dynamics

TeraChem was born in 2008 with the goal of providing fast on-the-fly electronic structure calculations to facilitate ab initio molecular dynamics studies of large biochemical systems such as photoswitchable proteins and multichromophoric antenna complexes. Originally developed for videogaming applications, graphics processing units (GPUs) offered a low-cost parallel computer architecture that became more accessible for general-purpose GPU computing with the release of CUDA in 2007. The evaluation of the electron repulsion integrals (ERIs) is a major bottleneck in electronic structure codes and provides an attractive target for acceleration on GPUs. Thus, highly efficient routines for evaluation of and contractions between the ERIs and density matrices were implemented in TeraChem. Here, electronic structure methods were developed and implemented to leverage these integral contraction routines, resulting in the first quantum chemistry package designed from the ground up for GPUs. This GPU acceleration makes TeraChem capable of performing large-scale ground and excited state calculations in the gas and condensed phase. Today, TeraChem's speed forms the basis for a suite of quantum chemistry applications, including optimization and dynamics of proteins, automated and interactive chemical discovery tools, and large-scale nonadiabatic dynamics simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2021

AI-driven multiscale simulations illuminate mechanisms of SARS-CoV-2 spike dynamics

We develop a generalizable AI-driven workflow that leverages heterogeneous HPC resources to explore the time-dependent dynamics of molecular systems. We use this workflow to investigate the mechanisms of infectivity of the SARS-CoV-2 spike protein, the main viral infection machinery. Our workflow enables more efficient investigation of spike dynamics in a variety of complex environments, including within a complete SARS-CoV-2 viral envelope simulation, which contains 305 million atoms and shows strong scaling on ORNL Summit using NAMD. We present several novel scientific discoveries, including the elucidation of the spike’s full glycan shield, the role of spike glycans in modulating the infectivity of the virus, and the characterization of the flexible interactions between the spike and the human ACE2 receptor. We also demonstrate how AI can accelerate conformational sampling across different systems and pave the way for the future application of such methods to additional studies in SARS-CoV-2 and other molecular systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Quantum-centric supercomputing for materials science: A perspective on challenges and future directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Computationally hard tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their resources for simulation, analysis, and data processing. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. Here in this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Timing of pulsars in the globular cluster omega centauri

We present the timing of the first five millisecond pulsars discovered in the globular cluster Omega Centauri and the discovery of a pulsar with a spin period of 3.68 ms. With a timing baseline of ~3.5 yr we are able to measure the derivative of the spin frequency ($\dot{\nu }$) for the first five pulsars. Upper limits on the pulsar line-of-sight acceleration are estimated and compared with predictions based on analytical models of the cluster. We find that PSRs J1326-4728B and D show large negative accelerations, which are in tension with the minimum acceleration predicted by analytical models. We searched for pulsed γ-ray signals using 14.3 yr of data from the Fermi Large Area Telescope. Although we found no evidence for γ-ray pulsations, PSRs J1326-4728A, B, C, and E are associated with X-ray sources. This suggests that the observed γ-ray emission from Omega Centauri is likely caused by the emission of the ensemble of MSPs. Finally, the linearly polarized emission from PSR J1326-4728A yields a rotation measure of -18 ± 8 rad m -2 .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Phylogenomic discovery and engineering of nitrogen fixation into the bioenergy woody crop poplar

Biological nitrogen fixation (BNF) is a key process enabling plants in specific lineages to convert atmospheric dinitrogen (N₂) into bioavailable ammonia through symbioses with diazotrophic microbes. Expanding this capability beyond native nitrogen-fixing clades into non-nodulating crops would reduce synthetic fertilizer use, lowering energy inputs and environmental impacts in agriculture. Supported by DOE Funding Award DE-SC0018247, the NitFix project advanced foundational knowledge required to engineer root-nodule symbioses in new host species. The team generated the most comprehensive phylogenomic analysis to date of all known nodulating lineages, resolving the evolutionary history of nitrogen-fixing symbiosis and identifying core gene suites retained across nodulating taxa. Through multimodal genomics, transcriptomics, and functional analyses in Medicago truncatula and related species, the project mapped regulatory networks underlying nodule organogenesis, bacterial infection, and nitrogen-fixation efficiency. Key discoveries include the identification of conserved signaling modules for rhizobial recognition, transcription factors controlling nodule differentiation, and metabolic pathways integrating fixed nitrogen into plant growth. The project also developed enabling tools—including optimized transformation pipelines, gene-editing workflows, and imaging-based phenotyping—to accelerate engineering efforts in emerging models. Together, these results refine the mechanistic framework of symbiotic nitrogen fixation and highlight transferable components essential for rewiring these traits into non-nodulating crops.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Mass Spectrometry-Based Approaches for Clinical Biomarker Discovery in Traumatic Brain Injury

Purpose of Review: Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Recent Findings: Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Summary: Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. In conclusion, while MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Deployment of inference as a service at the US CMS Tier-2 data centers

Coprocessors, especially GPUs, will be a vital ingredient of data production workflows at the HL-LHC. At CMS, the GPU-as-a-service approach for production workflows is implemented by the SONIC project (Services for Optimized Network Inference on Coprocessors). SONIC provides a mechanism for outsourcing computationally demanding algorithms, such as neural network inference, to remote servers, where requests from multiple clients are intelligently distributed across multiple GPUs by a load-balancing service. This talk highlights the recent progress in deploying SONIC at selected U.S. CMS Tier-2 data centers. Using realistic CMS Run3 data processing workflows, such as those containing transformer-based algorithms, we demonstrate how SONIC is integrated into the production-like environment to enable accelerated inference offloading. We will present developments from both the client and server sides, including production job and data center configurations for NVIDIA and AMD GPUs. We will also present performance scaling benchmarks and discuss the challenges of operating SONIC in CMS production, such as server discovery, GPU saturation, fallback server logic, etc.

Holzman, Burt↗

DOE OSTI2021

Effect of substitutional doping and disorder on the phase stability, magnetism, and half-metallicity of Heusler alloys

Spintronics is the fast growing field that will play a key role in optimizing power consumption, memory, and processing capabilities of nanoelectronic devices. Heusler alloys are potential candidates for application in spintronics due to their room temperature (RT) half-metallicity, high Curie temperature, low lattice mismatch with most substrates, and strong control on electronic density of states at Fermi level. In this work, we investigate the effect of substitutional doping and disorder on the half-metallicity, phase stability, and magnetism of Heusler alloys using density functional theory methods. Our study shows that electronic and magnetic properties of half/full-Heusler alloys can be tuned by changing electron-count through controlled variation of chemical compositions of alloying elements. We provide a detailed discussion on the effect of substitutional doping and disorder on the tunability of half-metallic nature of Co 2 MnX and NiMnX based Heusler alloys, where X represents group 13–16 and period 3–6 elements of the periodic table. Based on the idea of electron count and disorder, we predicted a possible existence of thermodynamically stable half-metallic multicomponent bismuthides, for example, (CuNi 3 )Mn 4 Bi 4 and (ZnNi 7 )Mn 8 Bi 8 , through substitution doping at Ni site by specific Cu and Zn composition in half-Heusler NiMnBi. We believe that the design guide based on electron-counts presented for half-metals will play a key role in electronic-structure engineering of novel Heusler alloys for spintronic application, which will accelerate the development and synthesis of novel materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Terahertz-field activation of polar skyrons

Unraveling collective modes arising from coupled degrees of freedom is crucial for understanding complex interactions in solids and developing new functionalities. Unique collective behaviors emerge when two degrees of freedom, ordered on distinct length scales, interact. Polar skyrmions, three-dimensional electric polarization textures in ferroelectric superlattices, disrupt the lattice continuity at the nanometer scale with nontrivial topology, leading to previously unexplored collective modes. Here, using terahertz-field excitation and femtosecond x-ray diffraction, we discover subterahertz collective modes, dubbed “skyrons”, which appear as swirling patterns of atomic displacements functioning as atomic-scale gearsets. The key to activating skyrons is the use of the THz field that couples primarily to skyrmion domain walls. Momentum-resolved time-domain measurements of diffuse scattering reveal an avoided crossing in the dispersion relation of skyrons. Atomistic simulations and dynamical phase-field modeling provide microscopic insights into the three-dimensional crystallographic and polarization dynamics. The amplitude and dispersion of skyrons are demonstrated to be controlled by sample temperature and electric-field bias. The discovery of skyrons and their coupling with terahertz fields opens avenues for ultrafast control of topological polar structures.

ferroelectrics↗

DOE OSTI2022

Understanding, discovery, and synthesis of 2D materials enabled by machine learning

Machine learning (ML) is becoming an effective tool for studying 2D materials. Taking as input computed or experimental materials data, ML algorithms predict the structural, electronic, mechanical, and chemical properties of 2D materials that have yet to be discovered. Such predictions expand investigations on how to synthesize 2D materials and use them in various applications, as well as greatly reduce the time and cost to discover and understand 2D materials. This tutorial review focuses on the understanding, discovery, and synthesis of 2D materials enabled by or benefiting from various ML techniques. Here, we introduce the most recent efforts to adopt ML in various fields of study regarding 2D materials and provide an outlook for future research opportunities. The adoption of ML is anticipated to accelerate and transform the study of 2D materials and their heterostructures.

2D Materials↗