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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Virtual Energetic Particle Observatory (VEPO)

The Virtual Energetic Particle Observatory (VEPO) focuses on improved discovery, access, and usability of heliospheric energetic particle and ancillary data products from selected spacecraft and sub-orbital instruments of the heliophysics data environment. The energy range of interest extends over the full range of particle acceleration from keV energies of suprathermal seed particles to GeV energies of galactic cosmic ray particles. Present spatial coverage is for operational and legacy spacecraft operating from the inner to the outer heliosphere, e.g. from measurements by the two Helios spacecraft to 0.3 AU to the inner heliosheath region now being traversed by the two Voyager spacecraft. This coverage will eventually be extended inward to ten solar radii by the planned NASA solar probe mission and at the same time beyond the heliopause into the outer heliosheath by continued Voyager operations. The geospace fleet of spacecraft providing near-Earth interplanetary measurements, selected magnetospheric spacecraft providing direct measurements of penetrating interplanetary energetic particles, and interplanetary cruise measurements from planetary spacecraft missions further extend VEPO resources to the domain of geospace and planetary interactions. Ground-based (e.g., neutron monitor) and high-altitude suborbital measurements can expand coverage to the highest energies of galactic cosmic rays affected by heliospheric interaction and of solar energetic particles. Science applications include investigation of solar flare and coronal mass ejection events. acceleration and transport of interplanetary particles within the inner heliosphere, cosmic ray interactions with planetary surfaces and atmospheres, sources of suprathermal and anomalous cosmic ray ions in the outer heliosphere, and solar cycle modulation of galactic cosmic rays. Robotic and human exploration, and eventual habitation, of planetary and space environments beyond the Earth require knowledge of radiation hazards informed by VEPO data resources. The VEPO project has completed the first year of work to define science requirements, to document and register selected data products in SPASE format while evolving SPASE for increased applicability to VEPO data, and to support enhanced discovery and access for these products through the evolving data query and middleware system of the Virtual Heliospheric Observatory (VHO). The VEPO team operates as a heliophysics focus group for energetic particle data resources in partnership with VHO and also leverages existing data services of NASA's Space Physics Data Facility. We invite comments from the U.S. and international data provider and user communities on review of the current VEPO/VHO user interface, on directions for future evolution of VEPO and supporting data systems including VHO and SPDF, and on relations to other elements of the heliophysics virtual observatory environment.

Cooper, John F.↗
DOE OSTI2025

SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions

Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and multimodal parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, material structure image spaces, and molecular embedding spaces. Often these systems are black-boxes and time-consuming to evaluate, which resulted in strong interest towards active learning methods such as Bayesian optimization (BO). However, these systems are often noisy which make the black box function severely multi-modal and non-differentiable, where a vanilla BO can get overly focused near a single or faux optimum, deviating from the broader goal of scientific discovery. To address these limitations, here we developed Strategic Autonomous Non-Smooth Exploration (SANE) to facilitate an intelligent Bayesian optimized navigation with a proposed cost-driven probabilistic acquisition function to find multiple global and local optimal regions, avoiding the tendency to becoming trapped in a single optimum. To distinguish between a true and false optimal region due to noisy experimental measurements, a human (domain) knowledge driven dynamic surrogate gate is integrated with SANE. We implemented the gate-SANE into pre-acquired piezoresponse spectroscopy data of a ferroelectric combinatorial library with high noise levels in specific regions, and piezoresponse force microscopy (PFM) hyperspectral data. SANE demonstrated better performance than classical BO to facilitate the exploration of multiple optimal regions and thereby prioritized learning with higher coverage of scientific values in autonomous experiments. Our work showcases the potential application of this method to real-world experiments, where such combined strategic and human intervening approaches can be critical to unlocking new discoveries in autonomous research.

Biswas, Arpan [University of Tennessee, Knoxville,↗
DOE OSTI2021

DOE Energy Frontier Research Centers Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio)

New capabilities to predict, design and control the chemistries of carbon could answer a global imperative to transition from fossil-based to sustainable transportation fuels. While the use of inexpensive hydrocarbons has been an unparalleled achievement and enabler of economic prosperity for many nations, singular dependence upon crude oil has given rise to systemic vulnerabilities in climate, energy, economic, and national security. Lignocellulosic biomass, a renewable and carbon-neutral resource, has the potential to displace an estimated annual equivalent of three billion barrels of oil in the U.S. alone (National Research Council 2009, U. S. Department of Energy, 2011). However, biomass has only one-third the energy density of crude oil (Agrawal and Singh 2009, Richard 2010) and lacks petroleum’s versatility as a feedstock for fuels and chemicals. These limitations keep biomass conversion below the efficiency level needed for strategic impact while the scientific challenge of routing carbon from one molecular context to another remains unmet. In 2009, the Center for Direct Catalytic Conversion of Biomass to Biofuels (C3Bio) recognized the potential of chemical catalysis and fast pyrolysis to overcome such limitations by transforming the main components of biomass (cellulose, xylan, and lignin) from grasses and trees directly to liquid hydrocarbons and aromatic co-products. Enabled by the EFRC high-risk, high-reward approach to grand challenge science, C3Bio researchers have been key national players in disrupting the conventional paradigm of the cellulosic biorefinery into a new future of “no carbon left behind”—the full utilization of carbon from plant cell walls in energy-dense fuels (Fig. 1). We identified catalytic and fast-pyrolytic pathways that utilize cellulose, xylan and, most significantly, lignin. We developed catalytic processes that deoxygenate and transform monomers and isolated polymers into useful products and tested their use with intact biomass. We gained control of lignin synthesis within plants and initiated tailoring biomass to its end-use through the tools of plant molecular biology and genetic engineering. C3Bio breakthroughs have increased the energy density of biomass-derived substrates via catalytic and pyrolytic conversions into products such as benzoquinones, furfural and hydroxymethylfurfural, levoglucosan and levulinic acid, methoxypropylphenols and propylbenzene. Such advances in biomass conversion would not have been possible without simultaneous advances in analytical instrumentation and methodologies, and imaging technologies and applications. The legacy science developed by C3Bio enables design and control strategies for achieving a targeted product portfolio of fuel and chemical feedstocks from a diverse range of native and tailored biomass. Our research provides the knowledge base required for a bio-economy with product streams as diverse in functionality as those of the petrochemical industry. Coupling targeted computational modeling with experimentation, we achieved: (1) fundamental understanding of biopolymers and cell wall architecture assembly, (2) discovery of new chemistries that allow the development of highly selective pathways to fuels and desirable chemicals, and (3) an integrated systems-level understanding to control catalytic and pyrolytic pathways.

09 BIOMASS FUELS↗
NASA NTRS2011

Magnetars: Challenging the Extremes of Nature

Magnetars are magnetically powered rotating neutron stars with extreme magnetic fields (over 10(exp 14) Gauss). They are discovered and emit predominantly in the X- and gamma-rays. Very few sources (roughly 15) have been found since their discovery in 1987. NASA s Fermi Observatory was launched June 11, 2009; the Fermi Gamma Ray Burst Monitor (GBM) began normal operations on July 14, about a month after launch, when the trigger algorithms were enabled. Since then, we recorded emission from four magnetar sources; of these, only one was an old magnetar: SGR 1806+20. The other three detections were two brand new sources, SGR J0501+4516, discovered with Swift and extensively monitored with both Swift and GBM, SGR J0418+5729, discovered with GBM and the Interplanetary Network (IPN), and SGR J1550-5418, a source originally classified as an Anomalous X-ray Pulsar (AXP 1E1547.0-5408). In my talk I will give a short history of the discovery of magnetars and describe how this, once relatively esoteric field, has emerged as a link between several astrophysical areas including Gamma-Ray Bursts. Finally, I will describe the properties of these sources and the current status of our knowledge of the magnetar population and birth rate.

Kouveliotou, Chryssa↗
NASA NTRSDate not supplied

Improving Earth Science Dataset Search with Publication

The NASA Goddard Earth Sciences Data and Information Services Center (GESDISC) archives a large number of Earth observational datasets. Thousands of the publications are created each year based on these datasets. The content of these publications can be used for discovery of the datasets based on the characteristics of applicational research. We leverage the content of these publications to retrieve the information about phenomena and domains where measurements from the datasets were utilized through linking these publications and dataset in Knowledge Graph. We retrieve phenomena and domain information using SWEET ontology and produce the set of keywords that are linked to the datasets. Further, we evaluate this link strength according to the frequency of dataset usage in the papers mentioning these keywords. We demonstrate how this linkage can improve dataset search by comparing the search results obtained from Common Metadata Repository (CMR) search and the publications based data.

Kristina Stoyanova↗
NASA NTRSDate not supplied

Science, Exploration and the Human Experience.

Dr. Darlene S. Lim is a research scientist at NASA's Ames Research Center in California's Silicon Valley, and the Deputy Project Scientist and Science Operations Lead for NASA's Volatiles Investigating Polar Exploration Rover (VIPER) Lunar Mission. The VIPER lunar rover mission is a mobile robot that will go to the South Pole of the Moon to get a close-up view of the location and concentration of water ice that could eventually be harvested to sustain human exploration on the Moon, Mars — and beyond. VIPER represents the first resource mapping mission on another celestial body and presents a unique operational paradigm within the history of robotic spaceflight. Throughout her career, Darlene has led several NASA-funded programs that have been focused on blending field science research with the development of capabilities and concepts for future human-robotic spaceflight to the Moon and Mars. These research activities have taken her around the world, from pole to pole, land to sea, and to many lakes in between, where she has studied life in extreme environments, the way people conduct science, and how they need to be supported in their work of discovery and exploration. At the center of each of these endeavors is the human - the person who must interact with others, with technology, and with the environment of interest, to apply the scientific method towards the accrual and expansion of knowledge about the planet that we live on and beyond. Darlene has been working with teams of scientists and engineers from a variety of disciplines to hone her understanding of the process by which their interactions and research needs can be supported and enabled under intense operational conditions such as those associated with Moon and Mars exploration. Darlene’s presentation will take the audience through the arc of her research over the past two decades culminating in her on-going work with the NASA VIPER lunar rover mission.

Darlene Sze Shien Lim↗
NASA NTRSDate not supplied

Increasing Discovery and Usability of Earth Science Satellite Data with My NASA Data

For 20 years, the My NASA Data project at NASA Langley Research Center has developed innovative approaches to increase the use of NASA’s satellite data by learners. My NASA Data offers a variety of authentic Earth Science datasets and a data visualization tool, eliminating the need for educators and/or learners to obtain specialized knowledge of GIS data formats and software to access and use authentic Earth Science data. While there is no shortage of available data, as federal government agencies such as NASA house petabytes of freely accessible Earth Science datasets, much of the data are only available for download and visualization in specialized formats and software, limiting their accessibility to educators and learners, especially those in primary and secondary school. Using the Google Earth Engine platform, the My NASA Data team has recently reinvented their data visualization tool, called the Earth System Data Explorer (ESDE). The ESDE gives users the capability to explore over 60 Earth Science satellite datasets in a multitude of formats such as maps, graphs, and data table Its new and improved user interface design was developed based on the preferences of educators, whom the My NASA Data project has over 20 years’ experience working with. Earth Science and GIS Subject Matter Experts (SMEs) structured the data in a professional and scientific manner. During Fiscal Year 2023, the My NASA Data website received over 1 million digital engagements, with over one-third being visitors to the data visualization tool. These metrics highlight the interest in a visualization tool that is simple and free to use with reliable and trusted datasets. The ESDE empowers users to readily relate and analyze NASA Earth Science data within their area of interest. The team used a user-centered design (UCD) framework to receive and incorporate feedback into the application’s design. Core requested features include the ability to create time series graphs, comparative analysis of maps, and download the data as CSV file. Responses indicate that advances in data visualization tools such as the ESDE make authentic Earth Science data more accessible. This presentation will cover how the My NASA Data project develops tools to enhance data discovery and accessibility, as well as how SME and user suggestions are incorporated.

Desiray Wilson↗
DOE OSTI2021

Advancing Electrolyte Solution Chemistry and Interfacial Electrochemistry of Divalent Metal Batteries

Divalent metal (Mg, Ca, etc.) battery chemistries potentially provide a sustainable long-term technical solution for large-scale energy storage because of high natural abundance of divalent metal elements in the earth crust. Good progress has been made on materials especially electrolyte development in the past years; however, significant challenges exist, particularly the very limited fundamental understanding on electrolyte solution chemistry and interfacial electrochemistry. In this perspective, we review and discuss key discoveries and understanding of divalent battery chemistry with a focus on electrolyte-dependent interfacial electrochemistry of divalent metal anodes. A concise review of electrolyte development, operando studies of the electrified interfaces and unique charge-transfer process is provided; the knowledge gaps and future research directions are discussed.

25 ENERGY STORAGE↗
DOE OSTI2021

Sugar transporters in grasses: Function and modulation in source and storage tissues

Carbohydrate partitioning, the process of transporting carbohydrates from photosynthetic (source) tissues, such as leaves, to non-photosynthetic (sink) tissues, such as stems, roots, and reproductive structures, is vital not only for the growth and development of plants but also for withstanding biotic and abiotic stress. In many plants, sucrose is the primary form of carbohydrate loaded into the phloem for long-distance transport and unloaded into the sink tissues for utilization or storage. Here, we highlight recent findings about 1) phloem loading in grasses, 2) the principal families of sugar transporters involved in sucrose transport, and 3) novel mechanisms by which the activities of sugar transporters are modulated. We discuss exciting discoveries from eudicot species that provide valuable insights regarding the regulation of these sugar transporters, which may be translatable to monocot species. As we better understand the intricate pathways that control the activities of various sugar transporters, we can utilize this knowledge for developing improved crop varieties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CACTUS: Chemistry Agent Connecting Tool Usage to Science

Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1997

The Lunar Atmosphere: History, Status, Current Problems, and Context

After decades of speculation and fruitless searches, the lunar atmosphere was first observed by Apollo surface and orbital instruments between 1970 and 1972. With the demise of Apollo in 1972, and the termination of funding for Apollo lunar ground station studies in 1977, the field withered for many years, but has recently enjoyed a renaissance. This reflowering has been driven by the discovery and exploration of sodium and potassium in the lunar exosphere by groundbased observers, the detection of metal ions derived from the Moon in interplanetary space, the possible discoveries of H2O ice at the poles of the Moon and Mercury, and the detections of tenuous atmospheres around more remote sites in the solar system, including Mercury and the Galilean satellites. In this review we summarize the present state of knowledge about the lunar atmosphere, describe the important physical processes taking place within it, and then discuss related topics including a comparison of the lunar atmosphere to other surface boundary exospheres in the solar system.

Stern, S. Alan .↗
NASA NTRS2013

The Gravity Recovery and Interior Laboratory Mission

The Gravity Recovery and Interior Laboratory (GRAIL) mission, launched in September 2011, successfully completed its Primary Science Mission in June 2012 and is currently in Extended Mission operations. Competitively selected under a NASA Announcement of Opportunity in December 2007, GRAIL is a Discovery Program mission subject to a mandatory project cost cap. The purpose of the mission is to precisely map the gravitational field of the Moon to reveal its internal structure from crust to core, determine its thermal evolution, and extend this knowledge to other planets. The mission uses twin spacecraft flying in tandem to provide the gravity map. The GRAIL Flight System, consisting of the spacecraft and payload, was developed based on significant heritage from previous missions such an experimental U.S. Air Force satellite, the Mars Reconnaissance Orbiter (MRO) mission, and the Gravity Recovery and Climate Experiment (GRACE) mission. The Mission Operations System (MOS) was based on high-heritage multimission operations developed by NASA's Jet Propulsion Laboratory and Lockheed Martin. Both the Flight System and MOS were adapted to meet the unique challenges posed by the GRAIL mission design. This paper summarizes the implementation challenges and accomplishments of getting GRAIL ready for launch. It also discusses the in-flight challenges and experiences of operating two spacecraft, and mission results.

GRAIL↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
NASA NTRS1988

RF surface resistance of YBa2Cu3O(7-x) thin films

The excitement engendered by the discovery of the new T sub c oxide superconductors has led to much speculation about practical applications of thin films of these materials in digital and analog electronic devices. Most of these envisioned applications involve high frequency signals for which a detailed knowledge of the surface impedance of the novel superconductors is very important. We have measured the surface resistance of thin films of YBaCuO in the frequency range 0.5 less than f less than 17 GHz using a stripline-resonator method. The stripline procedure also was used to measure the surface resistance of high quality gold and aluminum films; the resistance values obtained agree with values predicted from the measured dc resistance using the Pippard formalism for the anomalous skin effect. The YBaCuO were produced by a multilayer deposition process. The films are formed by e-beam evaporation of 24 layers of Y, Ba, and Cu. Films with the highest transition temperature were obtained using yttria-stabilized zirconia (YSZ) substrates. After deposition, the films are transferred to a furnace where they are annealed in flowing O2 at 850 C for 2h. The furnace then is turned off and allowed to cool to 100 C in about 16 h. Auger profiling of the films made by this process shows that the concentrations of Y, Ba, Cu, and O are uniform to within 1 percent throughout the thickness of the film.

Source record↗
NASA NTRS2008

Quantitative Studies of the Optical and UV Spectra of Galactic Early B Supergiants

We undertake an optical and ultraviolet spectroscopic analysis of a sample of 20 Galactic B0-B5 supergiants of luminosity classes Ia, Ib, Iab, and II. Fundamental stellar parameters are obtained from optical diagnostics and a critical comparison of the model predictions to observed UV spectral features is made. Methods. Fundamental parameters (e.g., T(sub eff), log L(sub *), mass-loss rates and CNO abundances) are derived for individual stars using CMFGEN, a nLTE, line-blanketed model atmosphere code. The impact of these newly derived parameters on the Galactic B supergiant Ten scale, mass discrepancy, and wind-momentum luminosity relation is examined. Results. The B supergiant temperature scale derived here shows a reduction of about 1000-3000 K compared to previous results using unblanketed codes. Mass-loss rate estimates are in good agreement with predicted theoretical values, and all of the 20 BO-B5 supergiants analysed show evidence of CNO processing. A mass discrepancy still exists between spectroscopic and evolutionary masses, with the largest discrepancy occuring at log (L/(solar)L approx. 5.4. The observed WLR values calculated for B0-B0.7 supergiants are higher than predicted values, whereas the reverse is true for B1-B5 supergiants. This means that the discrepancy between observed and theoretical values cannot be resolved by adopting clumped (i.e., lower) mass-loss rates as for O stars. The most surprising result is that, although CMFGEN succeeds in reproducing the optical stellar spectrum accurately, it fails to precisely reproduce key UV diagnostics, such as the N v and C IV P Cygni profiles. This problem arises because the models are not ionised enough and fail to reproduce the full extent of the observed absorption trough of the P Cygni profiles. Conclusions. Newly-derived fundamental parameters for early B supergiants are in good agreement with similar work in the field. The most significant discovery, however, is the failure of CMFGEN to predict the correct ionisation fraction for some ions. Such findings add further support to revising the current standard model of massive star winds, as our understanding of these winds is incomplete without a precise knowledge of the ionisation structure and distribution of clumping in the wind. Key words. techniques: spectroscopic - stars: mass-loss - stars: supergiants - stars: abundances - stars: atmospheres - stars: fundamental parameters

Searle, S. C.↗
DOE OSTI2023

A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data, and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases are either not designed for natural products data, or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data. Recently, our team reported the development of the Natural Products Magnetic Resonance Database (NP-MRD; www.np-mrd.org). Here in this paper we present a new data deposition platform for the NP-MRD project that is designed to enable users to deposit NMR data for published or submitted manuscripts in under five minutes. This platform includes a suite of automated data extraction and standardization tools, together with a simple-to-use web-based interface and detailed error reporting to simplify the data deposition process and is available at www.np-mrd.org/submissions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction

The information content of crystalline materials becomes astronomical when collective electronic behavior and their fluctuations are taken into account. In the past decade, improvements in source brightness and detector technology at modern X-ray facilities have allowed a dramatically increased fraction of this information to be captured. Now, the primary challenge is to understand and discover scientific principles from big datasets when a comprehensive analysis is beyond human reach. We report the development of an unsupervised machine learning approach, X-ray diffraction (XRD) temperature clustering (X-TEC), that can automatically extract charge density wave order parameters and detect intraunit cell ordering and its fluctuations from a series of high-volume X-ray diffraction measurements taken at multiple temperatures. We benchmark X-TEC with diffraction data on a quasi-skutterudite family of materials, (Ca x Sr 1–x ) 3 Rh 4 Sn 13 , where a quantum critical point is observed as a function of Ca concentration. We apply X-TEC to XRD data on the pyrochlore metal, Cd 2 Re 2 O 7 , to investigate its two much-debated structural phase transitions and uncover the Goldstone mode accompanying them. We demonstrate how unprecedented atomic-scale knowledge can be gained when human researchers connect the X-TEC results to physical principles. Specifically, we extract from the X-TEC–revealed selection rules that the Cd and Re displacements are approximately equal in amplitude but out of phase. This discovery reveals a previously unknown involvement of 5d 2 Re, supporting the idea of an electronic origin to the structural order. Our approach can radically transform XRD experiments by allowing in operando data analysis and enabling researchers to refine experiments by discovering interesting regions of phase space on the fly.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ultimate Colliders

Our understanding of the Universe critically depends on the fundamental knowledge of particles and fields, which represents a central endeavor of modern high-energy physics. Energy frontier particle colliders - arguably, among the largest, most complex and advanced scientific instruments of modern times - for many decades have been at the forefront of scientific discoveries in high-energy physics. Due to technology advances and beam physics breakthroughs, the colliding beam facilities have progressed immensely and now operate at energies and luminosities many orders of magnitude greater than the pioneering instruments of the early 1960s. While the Large Hadron Collider and the Super-KEKB factory represent the frontier hadron and lepton colliders of today, respectively, future colliders are an essential component of a strategic vision for particle physics. Conceptual studies and technical developments for several exciting near- and medium-term future collider options are underway internationally. Analysis of numerous proposals and studies for far-future colliders indicate the limits of the collider beam technology due to machine size, cost, and power consumption, and call for a paradigm shift of the particle physics research at ultra-high energy but low luminosity colliders approaching or exceeding 1 PeV center-of-mass energy scale.

43 PARTICLE ACCELERATORS↗