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NASA Tech Briefs, February 2007

Topics covered include: Calibration Test Set for a Phase-Comparison Digital Tracker; Wireless Acoustic Measurement System; Spiral Orbit Tribometer; Arrays of Miniature Microphones for Aeroacoustic Testing; Predicting Rocket or Jet Noise in Real Time; Computational Workbench for Multibody Dynamics; High-Power, High-Efficiency Ka-Band Space Traveling-Wave Tube; Gratings and Random Reflectors for Near-Infrared PIN Diodes; Optically Transparent Split-Ring Antennas for 1 to 10 GHz; Ice-Penetrating Robot for Scientific Exploration; Power-Amplifier Module for 145 to 165 GHz; Aerial Videography From Locally Launched Rockets; SiC Multi-Chip Power Modules as Power-System Building Blocks; Automated Design of Restraint Layer of an Inflatable Vessel; TMS for Instantiating a Knowledge Base With Incomplete Data; Simulating Flights of Future Launch Vehicles and Spacecraft; Control Code for Bearingless Switched- Reluctance Motor; Machine Aided Indexing and the NASA Thesaurus; Arbitrating Control of Control and Display Units; Web-Based Software for Managing Research; Driver Code for Adaptive Optics; Ceramic Paste for Patching High-Temperature Insulation; Fabrication of Polyimide-Matrix/Carbon and Boron-Fiber Tape; Protective Skins for Aerogel Monoliths; Code Assesses Risks Posed by Meteoroids and Orbital Debris; Asymmetric Bulkheads for Cylindrical Pressure Vessels; Self-Regulating Water-Separator System for Fuel Cells; Self-Advancing Step-Tap Drills; Array of Bolometers for Submillimeter- Wavelength Operation; Delta-Doped CCDs as Detector Arrays in Mass Spectrometers; Arrays of Bundles of Carbon Nanotubes as Field Emitters; Staggering Inflation To Stabilize Attitude of a Solar Sail; and Bare Conductive Tether for Decelerating a Spacecraft.

Source record↗

Modeling Flight: The Role of Dynamically Scaled Free-Flight Models in Support of NASA's Aerospace Programs

The state of the art in aeronautical engineering has been continually accelerated by the development of advanced analysis and design tools. Used in the early design stages for aircraft and spacecraft, these methods have provided a fundamental understanding of physical phenomena and enabled designers to predict and analyze critical characteristics of new vehicles, including the capability to control or modify unsatisfactory behavior. For example, the relatively recent emergence and routine use of extremely powerful digital computer hardware and software has had a major impact on design capabilities and procedures. Sophisticated new airflow measurement and visualization systems permit the analyst to conduct micro- and macro-studies of properties within flow fields on and off the surfaces of models in advanced wind tunnels. Trade studies of the most efficient geometrical shapes for aircraft can be conducted with blazing speed within a broad scope of integrated technical disciplines, and the use of sophisticated piloted simulators in the vehicle development process permits the most important segment of operations the human pilot to make early assessments of the acceptability of the vehicle for its intended mission. Knowledgeable applications of these tools of the trade dramatically reduce risk and redesign, and increase the marketability and safety of new aerospace vehicles. Arguably, one of the more viable and valuable design tools since the advent of flight has been testing of subscale models. As used herein, the term "model" refers to a physical article used in experimental analyses of a larger full-scale vehicle. The reader is probably aware that many other forms of mathematical and computer-based models are also used in aerospace design; however, such topics are beyond the intended scope of this document. Model aircraft have always been a source of fascination, inspiration, and recreation for humans since the earliest days of flight. Within the scientific community, Leonardo da Vinci, George Cayley, and the Wright brothers are examples of early aviation pioneers who frequently used models during their scientific efforts to understand and develop flying machines. Progress in the technology associated with model testing in worldwide applications has firmly established model aircraft as a key element in new aerospace research and development programs. Models are now routinely used in many applications and roles, including aerodynamic data gathering in wind tunnel investigations for the analysis of full-scale aircraft designs, proof-of-concept demonstrators for radical aeronautical concepts, and problem-solving exercises for vehicles already in production. The most critical contributions of aerospace models are to provide confidence and risk reduction for new designs and to enhance the safety and efficiency of existing configurations.

Chambers, Joseph↗

MLCommons Science Benchmarks

Benchmarks are a cornerstone of modern machine learning practice, providing standardized eval- uations that enable reproducibility, comparison, and scientific progress. Yet, as AI systems particularly deep learning models become increasingly dynamic, traditional static benchmarking approaches are losing their relevance. Models rapidly evolve in architecture, scale, and capability; datasets shift; and deployment contexts continuously change, creating a moving target for evaluation. Without adaptive benchmarking frame- works, both scientific assessment and real-world de- ployment risk becoming misaligned with actual system behavior. Drawing on our experience from MLCommons, educa- tional initiatives, and government programs such as the DOE s Million Parameter Consortium, we identify key barriers that hinder the broader adoption and utility of benchmarking in AI. These include substantial resource demands, limited access to specialized hardware, lack of expertise in benchmark design, and uncertainty among practitioners about how to relate benchmark results to their own application domains. Moreover, current benchmarks often emphasize peak performance on leadership-class hardware, offering limited guidance for more diverse, real-world deployment scenarios. We argue that benchmarking itself must become dy- namic in order to incorporate evolving models, updated data, and heterogeneous computational platforms while maintaining transparency, reproducibility, and inter- pretability. Democratizing this process requires not only technical innovation, but also systematic educational efforts spanning undergraduate to professional levels to develop sustained expertise in benchmark design and use. Finally, benchmarks should be framed and com- municated to support application-relevant comparisons, enabling both developers and users to make informed, context-sensitive decisions. Advancing dynamic and inclusive benchmarking practices will be essential to ensure that evaluation keeps pace with the evolving AI landscape and supports responsible, reproducible, and accessible AI deployment.

Hawks, Benjamin G. [Fermilab]↗

Advances in X-ray Instruments to Support Mars Sample Return

The Mars 2020 Perseverance rover is currently collecting drill cores of ancient igneous and sedimentary rock in and around Jezero crater for potential transport to Earth. These samples from the martian surface will enable detailed mineralogical, geochemical, and petrological measurements to characterize ancient depositional and diagenetic environments, quantitatively age-date the samples, and identify the building blocks for life or evidence for life itself. Furthermore, these drill cores are especially precious because they may represent the most pristine samples from the martian surface and our best chance at identifying martian life, as future sample return missions may be conducted by humans that can introduce biological contaminants to the samples. Because of the importance of these samples, we must take great care in their handling, curation, and preliminary analyses so that they are preserved for scientific measurements for decades to come. In-situ measurements by Perseverance have identified minerals that further warrant special treatment of the returned samples. Hydrated sulfate carbonate, swelling clay minerals, and oxychlorine salts are extremely sensitive to changes in temperature and relative humidity. The structures of hydrated sulfates and oxychlorine minerals, in particular, readily change when exposed to different conditions, meaning the mineral assemblage of the as-returned samples may be lost if the samples aren’t handled properly. Characterizing the as-returned mineral assemblage, particularly of the salts, is essential for reconstructing past aqueous conditions and habitability. To characterize the as-returned mineral assemblage, the samples must be analyzed rapidly before phase changes occur and/or under controlled conditions (e.g., within a glove box). Significant recent advances in X-ray instrumentation for robotic exploration of the solar system have resulted in high-resolution miniaturized instruments that would provide mineralogical, geochemical, and petrological information on the returned martian samples without degradation of the mineral assemblage. Here, we describe a combined X-ray diffractometer/X-ray fluorescence spectrometer (XRD/XRF), an X-ray computed tomographic (XCT) instrument, and a scanned beam XRF mapping instrument that could be used in a glove box so that the martian samples remain under controlled conditions.

E. B. Rampe↗

Center for Gas Separations (CGS)

The total energy consumption in the U.S. has been rising steadily for decades, and it currently amounts to ~98,000 TBtu/yr, with approximately 30% of this total attributable to the industrial sector. Reasonable estimates indicate that 45–55% of total industry energy consumption derives from chemical separations, and for example, over 120 TBtu/yr alone is used in carrying out olefin/paraffin separations via energy-intensive cryogenic distillation. Therefore, the pursuit of new, even radically different approaches to some of the most energy-intensive industrial separations processes is an imperative scientific pursuit for reducing energy consumption toward a more sustainable future. Adsorbent and membrane-based separations can require a fraction of the energy needed for distillation methods, and as such are considered promising solutions for balancing increasing energy demand in the U.S. with the need for a massive reduction in energy consumption. Although considerable research effort has been devoted to the design of materials capable of carrying out various gas separations, usually operating through size-selective, chemisorptive, or physisorptive mechanisms, it remains a great challenge to design materials that function adequately for real-world applications. Indeed, the chemical and physical differences between molecules in gas mixtures of interest are often small, and therefore it is necessary, through the use of nanoscience and synthetic chemistry, to engineer unprecedented molecular-level control in adsorbate–adsorbent interactions. The overarching mission of the Center for Gas Separations (CGS) was to discover fundamental innovations that have the potential to dramatically reduce the energy associated with critical gas separations. In particular, the CGS developed novel synthetic routes, guided by molecular chemistry principles, as well as advanced characterization and computational methods, that have enabled the discovery of new materials and membranes tailor-made to exhibit exceptional performance for a range of gas separations processes, as required in the clean use of fossil fuels and in reducing CO 2 emissions from industry. A challenge of this magnitude required the collaboration and synergy of a large team of researchers with expertise in materials synthesis, characterization, and computations. During the 11-year project period, the CGS created a range of new materials within the family of highly-tunable, porous solids known as metal–organic frameworks (MOFs). These new frameworks demonstrate novel mechanisms for key industrial gas separations, including revolutionary new cooperative adsorption processes that enable low-energy CO 2 and CO capture, and are capable of efficiently separating olefins from paraffins, O 2 from air, and the shape-selective separation of alkane isomers. In addition, the CGS developed new strategies for incorporating these materials into composite membranes toward highly efficient and selective membrane-based separations. As a testament to the success of the CGS, two start-up companies, Mosaic Materials,4 Inc. and Flux Technology, Inc., grew out of these research efforts, and these companies are seeking to commercialize MOF and composite membranes materials for key separations in industry, including large-scale CO 2 capture and hydrocarbon separations, respectively. Another company, framergy, Inc., licensed IP resulting from CGS research toward the commercialization of adsorbents for various energy-relevant applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Advances in SPICE Support of Planetary Science

SPICE is the de facto international standard for determining the geometric conditions-parameters such as altitude, lighting angles, and LAT/LON coverage of an instrument footprint-pertaining to scientific observations acquired by instruments on board robotic spacecraft. This system, comprised of data and allied software, is used for planning science observations and for analyzing the data returned from those observations. Use of SPICE is not a NASA requirement but is recommended by NASA's Planetary Data System and by the International Planetary Data Alliance. Owing in part to its reliability, stability, portability and user support, the use of SPICE has spread to many national space agencies, including those of the U.S., Europe (ESA), Japan, Russia and India. SPICE has been in use since the Magellan mission to Venus and so has many well-known capabilities. But the NAIF Team responsible for implementing SPICE continues to add new features; this presentation describes a number of these.

computational capability↗

A Macintosh-Based Scientific Images Video Analysis System

A set of experiments was designed at MIT's Man-Vehicle Laboratory in order to evaluate the effects of zero gravity on the human orientation system. During many of these experiments, the movements of the eyes are recorded on high quality video cassettes. The images must be analyzed off-line to calculate the position of the eyes at every moment in time. To this aim, I have implemented a simple inexpensive computerized system which measures the angle of rotation of the eye from digitized video images. The system is implemented on a desktop Macintosh computer, processes one play-back frame per second and exhibits adequate levels of accuracy and precision. The system uses LabVIEW, a digital output board, and a video input board to control a VCR, digitize video images, analyze them, and provide a user friendly interface for the various phases of the process. The system uses the Concept Vi LabVIEW library (Graftek's Image, Meudon la Foret, France) for image grabbing and displaying as well as translation to and from LabVIEW arrays. Graftek's software layer drives an Image Grabber board from Neotech (Eastleigh, United Kingdom). A Colour Adapter box from Neotech provides adequate video signal synchronization. The system also requires a LabVIEW driven digital output board (MacADIOS II from GW Instruments, Cambridge, MA) controlling a slightly modified VCR remote control used mainly to advance the video tape frame by frame.

Groleau, Nicolas↗

Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments

Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.

Saranathan, Gayathri [Hewlett-Packard]↗

End-to-End Differentiable Modeling and Management of the Environment

Focal Area: (2) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization. We emphasize the importance of leveraging optimization techniques from AI/machine learning (ML) to solve challenging problems in Earth system modeling. Science Challenge: Automatic differentiation has had a transformative effect on ML by allowing the calculation of gradients of arbitrary functions in an incredibly large class of models. We can potentially realize similar improvements in parameter estimation and control for Earth system models (ESMs) by reimplementing them in computational frameworks from ML. Practitioners working with large (>10 7 parameters) models in ML and AI can obtain good predictive performance in a range of spatiotemporal tasks by making use of optimization via stochastic gradient descent and incorporating prior knowledge at multiple levels. We propose writing ESMs in open-source computational frameworks such as Torch, Tensorflow, and JAX to greatly expand the scope of environmental forecasting and management challenges, which can be addressed by leveraging automatic differentiation and gradient descent-like algorithms. We do not call for a wholesale replacement of physical models with data-driven surrogates, but rather advocate for interleaving physical and empirical equations in a manner that is most faithful to the extent of our scientific knowledge and observational data. Central to this topic is the merging of differentiable physical simulations with differentiable optimization layers, which are now both beginning to come to the forefront.

54 ENVIRONMENTAL SCIENCES↗

Optimal Transport as a Tool for Scientific Discovery in Radiation Biology

This report summarizes findings from research conducted for the “Exploration of the Poten tial for Artificial Intelligence and Machine Learning to Advance Low-Dose Radiation Biology Re search” (RadBio-AI) program, supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, under Awards KP1601011/FWP CC121 and KP1601017/FWP CC121. The research reported here was undertaken in an effort to assess the potential of optimal measure transport methods as components within the larger scope of a com putational framework envisioned to support research in the radiation biology domain. Within this effort, our interest centered on enabling a unified generic framework where probabilistic modeling, inference, and statistical learning can be carried out for a wide range of data distributions. As described next in Section 1 (and in more detail in our original publication), optimal measure transport offers the possibility of such unified approach.

97 MATHEMATICS AND COMPUTING↗

Progress of Cometary Science in the Past 100 Years

Enormous strides made by cometary science during the 20th century defy any meaningful comparison of its state 100 years ago and now. The great majority of the subfields enjoying much attention nowadays did not exist in the year 1900. Dramatic developments, especially in the past 30-50 years, have equally affected observational and theoretical studies of comets. The profound diversification of observing techniques has been documented by the ever widening limits on the electromagnetic spectrum covered. While the time around 1900 marked an early period of slow and painful experimentation with photographic methods in cometary studies, observations of comets from the x-ray region to the radio waves have by now become routine. Many of the new techniques, and all those involved with the wavelengths shorter than about 300 nm, were made possible by another major breakthrough of this century - observing from space. Experiments on dedicated Earth-orbiting satellites as well as several deep-space probes have provided fascinating new information on the nature and makeup of comets. In broader terms, much of the progress has been achieved thanks to fundamental discoveries and major advances in electronics, whose applications resulted in qualitatively new instruments (e.g. radiotelescopes) and sensors or detectors (e.g. CCD arrays). The most universal effect on the entire cometary science, from observing to data handling to quantitative interpretations, has been, as in any other branch of science, due to the introduction of electronic computers, with their processing capabilities not only unheard of, but literally unimaginable, in the age of classical desk calculators. As if all this should not be enough, the today's generations of comet scientists have, in addition, been blessed with nature's highly appreciated cooperation. Indeed, in the span of a dozen years, between 1985 and 1997, we were privileged to witness four remarkable cometary events: (i) a return of Halley's celebrated comet; (ii) the impact of an extensively fragmented comet Shoemaker-Levy 9 into Jupiter - a once-in-a-millenium episode; (iii) a closeup, appearance of a small but very active Earth-approaching comet Hyakutake; and (iv) an unforgettable celestial show of comet Hale-Bopp, one of the brightest and the most massive comets ever observed. We have been - and for many years to come will be - harvesting scientifically lucrative findings based on the flood of data accumulated during observational campaigns organized for these and other recent objects. Our understanding of cometary phenomena will continue to grow as observing techniques are further being improved and as more sophisticated theories are being developed. Recent accomplishments achieved in the rapidly expanding field of comets are certain to stimulate the members of this astronomical community in their quest for new discoveries in the 21st century!

Sekanina, Zdenek↗

Examples of Mission-driven Data Science from Jefferson Lab and ACES

This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.

McSpadden, Diana [Thomas Jefferson National Accele↗

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. Here, this study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure–temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

36 MATERIALS SCIENCE↗

Autonomy and the human element in space

NASA is contemplating the next logical step in the U.S. space program - the permanent presence of humans in space. As currently envisioned, the initial system, planned for the early 1990's, will consist of manned and unmanned platforms situated primarily in low Earth orbit. The manned component will most likely be inhabited by 6-8 crew members performing a variety of tasks such as materials processing, satellite servicing, and life science experiments. The station thus has utility in scientific and commercial enterprises, in national security, and in the development of advanced space technology. The technical foundations for this next step have been firmly established as a result of unmanned spacecraft missions to other planets, the Apollo program, and Skylab. With the shuttle, NASA inaugurates a new era of frequent flights and more routine space operations supporting a larger variety of missions. A permanently manned space system will enable NASA to expand the scope of its activities still further. Since NASA' s inception there has been an intense debate over the relative merits of manned and unmanned space systems. Despite the generally higher costs associated with manned components, astronauts have accomplished numerous essential, complex tasks in space. The unique human talent to evaluate and respond inventively to unanticipated events has been crucial in many missions, and the presence of crews has helped arouse and sustain public interest in the space program. On the other hand, the hostile orbital environment affects astronaut physiology and productivity, is dangerous, and mandates extensive support systems. Safety and cost factors require the entire station complex, both space and ground components, to be highly automated to free people from mundane operational chores. Recent advances in computer technology, artificial intelligence (AI), and robotics have the potential to greatly extend space station operations, offering lower costs and superior productivity. Extended operations can in turn enhance critical technologies and contribute to the competitive economic abilities of the United States. A high degree of automation and autonomy may be required to reduce dependence on ground systems, reduce mission costs, diminish complexity as perceived by the crew, increase mission lifetime and expand mission versatility. However, technologies dealing with heavily automated, long duration habitable spacecraft have not yet been thoroughly investigated by NASA. A highly automated station must amalgamate the diverse capabilities of people, machines, and computers to yield an efficient system which capitalizes on unique human characteristics. The station also must have an initial design which allows evolution to a larger and more sophisticated space presence. In the early years it is likely that AI-based subsystems will be used primarily in an advisory or planning capacity. As human confidence in automated systems grows and as technology advances, machines will take on more critical and interdependent roles. The question is whether, and how much, system autonomy will lead to improved station effectiveness.

Source record↗

A framework to evaluate machine learning crystal stability predictions

The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases.

Riebesell, Janosh↗

Foundation model framework for all tasks involving jet physics

Foundation models use large datasets to build an effective representation of data that can be deployed on diverse downstream tasks. Previous research developed the omnilearn foundation model for jet physics, using unique properties of particle physics, and showed that it could significantly advance discovery potential across collider experiments. This paper introduces a major upgrade, resulting in the omnilearned framework. This framework has three new elements: (1) updates to the model architecture and training, (2) using over 1 × 10 9 jets used for training, and (3) providing well-documented software for accessing all datasets and models. We demonstrate omnilearned with three representative tasks: top-quark jet tagging with the community delphes-based benchmark dataset, b tagging with ATLAS full simulation, and anomaly detection with CMS experimental data. In each case, omnilearned is the state of the art, further expanding the discovery potential of past, current, and future collider experiments.

Bhimji, Wahid [Lawrence Berkeley National Laborato↗

User applications driven by the community contribution framework MPContribs in the Materials Project

This paper discusses how the MPContribs framework in the Materials Project (MP) allows user-contributed data to be shown and analyzed alongside the core MP database. The MP is a searchable database of electronic structure properties of over 65,000 bulk solid materials, which is accessible through a web-based science-gateway. We describe the motivation for enabling user contributions to the materials data and present the framework's features and challenges in the context of two real applications. These use cases illustrate how scientific collaborations can build applications with their own 'user-contributed' data using MPContribs. The Nanoporous Materials Explorer application provides a unique search interface to a novel dataset of hundreds of thousands of materials, each with tables of user-contributed values related to material adsorption and density at varying temperature and pressure. The Unified Theoretical and Experimental X-ray Spectroscopy application discusses a full workflow for the association, dissemination, and combined analyses of experimental data from the Advanced Light Source with MP's theoretical core data, using MPContribs tools for data formatting, management, and exploration. The capabilities being developed for these collaborations are serving as the model for how new materials data can be incorporated into the MP website with minimal staff overhead while giving powerful tools for data search and display to the user community.

97 MATHEMATICS AND COMPUTING↗

EFRC 10@10: Developing the Next Generation of Scientific Talent: Michael Naguib

This is a biography of Michael Naguib, winner of the Workforce Development Award at the EFRC 10@10 Awards Ceremony in 2019. Michael Naguib was born in Egypt and completed his undergraduate and master’s degrees at Cairo University. He wanted to study advanced materials, but there was no place to do so in Egypt. Instead he enrolled in a PhD program at Drexel University in Philadelphia. There he was assigned to explore whether certain materials that have a layered structure (with three layers, each having a different chemical composition) could be used to make better electrodes for lithium ion batteries. Michael found that the materials were not useful as electrodes because lithium ions (the charge carriers in modern batteries) could not penetrate the material. To address this, he tried to modify the material by removing the middle layer with acid. The unexpected result was the discovery of a completely new nano-material: a titanium-carbon compound in the form of a two-dimensional sheet less than a nanometer thick. He and his advisors at Drexel immediately recognized the new material’s potential for energy storage. Publication of that discovery—of what soon became clear was a whole family of two-dimensional materials now called MXenes—triggered a world-wide surge of research. To explore MXenes and related phenomena in more detail, the U.S. Department of Energy’s Office of Science funded an Energy Frontier Research Center (EFRC) on Fluid Interface Reactions, Structures and Transport. The research team included computational modelers, university experts in materials synthesis and thermodynamic analysis, and scientists at DOE’s Oak Ridge National Laboratory (ORNL) who could probe the material’s structure with neutrons and powerful microscopes.

25 ENERGY STORAGE↗