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DOE OSTI2025

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗
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↗
DOE OSTI2016

Optimization of tomographic reconstruction workflows on geographically distributed resources

New technological advancements in synchrotron light sources enable data acquisitions at unprecedented levels. This emergent trend affects not only the size of the generated data but also the need for larger computational resources. Although beamline scientists and users have access to local computational resources, these are typically limited and can result in extended execution times. Applications that are based on iterative processing as in tomographic reconstruction methods require high-performance compute clusters for timely analysis of data. Here, time-sensitive analysis and processing of Advanced Photon Source data on geographically distributed resources are focused on. Two main challenges are considered: (i) modeling of the performance of tomographic reconstruction workflows and (ii) transparent execution of these workflows on distributed resources. For the former, three main stages are considered: (i) data transfer between storage and computational resources, (i) wait/queue time of reconstruction jobs at compute resources, and (iii) computation of reconstruction tasks. These performance models allow evaluation and estimation of the execution time of any given iterative tomographic reconstruction workflow that runs on geographically distributed resources. For the latter challenge, a workflow management system is built, which can automate the execution of workflows and minimize the user interaction with the underlying infrastructure. The system utilizes Globus to perform secure and efficient data transfer operations. The proposed models and the workflow management system are evaluated by using three high-performance computing and two storage resources, all of which are geographically distributed. Workflows were created with different computational requirements using two compute-intensive tomographic reconstruction algorithms. Experimental evaluation shows that the proposed models and system can be used for selecting the optimum resources, which in turn can provide up to 3.13× speedup (on experimented resources). Furthermore, the error rates of the models range between 2.1 and 23.3% (considering workflow execution times), where the accuracy of the model estimations increases with higher computational demands in reconstruction tasks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Progressive transfer learning for advancing machine learning-based reduced-order modeling

Abstract To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Probing Electron Correlations in 1D Electronic Materials Using Single Quantum Channels (Final Technical Report)

This research focuses on the investigation of strong electron correlations in quasi-one-dimensional oxide nanostructures. The archetypical system consisting of heterostructures formed from an ultrathin layer of LaAlO 3 and TiO 2 -terminated SrTiO 3 offers a unique laboratory for probing strong correlations due to intrinsic richness of the physical system and its ability to be modulated at extreme nanoscale dimensions. Under suitable conditions, SrTiO 3 is known to exhibit a variety of important properties associated with strong electronic correlations, especially superconductivity, magnetism. Understanding the nature of these electronic correlations at fundamental scales, and in reduced spatial dimensions, will provide the scientific basis for new energy technologies, information technologies, and materials. The research program is motivated by recent discoveries and technical advances in the PIs' lab, specifically, (1) pioneering work to create conductive nanostructures to be created in LaAlO 3 /SrTiO 3 heterointerfaces with 2 nm spatial resolution, (2) the discovery of a novel correlated electronic phases in which electrons pair without forming a superconductor, and (3) the demonstrated ability to form high-quality quantum point contacts (QPCs) and achieve ballistic electron transport over micrometer-scales in LaAlO 3 /SrTiO 3 nanowires. The program has three major components. First, we have press forward to develop a full microscopic understanding of the mechanism for electron pairing in LaAlO 3 /SrTiO 3 nanostructures. Understanding the “glue” that leads to pairing and superconductivity in a doped semiconductor will help to form a scientific basis for the correlated oxide nanoelectronics platform being developed. Second, we have used their ability to create well-defined quantum channels in nanowires allows to allow novel “scattering” experiments to be performed that probe fundamentally new quasiparticle excitations using an unprecedented combination of experimental methodologies. Experiments already underway with artificially defined superlattices (5 nm period) demonstrate the feasibility of this approach. Third, we have investigated 1D correlations in proximally coupled nanowires, which are predicted exhibit Majorana-like zero modes in the absence of superconductivity or spin-orbit coupling. These studies provide insight not only into new and emergent phenomena in the rich class of complex oxides, but also fundamental mechanisms of superconductivity and magnetism. Thematically, the research aims to combine some of the most important and interesting challenges in the physics of semiconductor nanostructures and correlated electronic materials. The increased understanding of electron correlations can pave the way to the development of new materials with outstanding or novel properties, which could be relevant for energy-related technologies such as low-power high performance computing, quantum computation, or the development of novel materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

AI-based design of a nuclear reactor core

The authors developed an artificial intelligence (AI)-based algorithm for the design and optimization of a nuclear reactor core based on a flexible geometry and demonstrated a 3× improvement in the selected performance metric: temperature peaking factor. The rapid development of advanced, and specifically, additive manufacturing (3-D printing) and its introduction into advanced nuclear core design through the Transformational Challenge Reactor program have presented the opportunity to explore the arbitrary geometry design of nuclear-heated structures. The primary challenge is that the arbitrary geometry design space is vast and requires the computational evaluation of many candidate designs, and the multiphysics simulation of nuclear systems is very time-intensive. Therefore, the authors developed a machine learning-based multiphysics emulator and evaluated thousands of candidate geometries on Summit, Oak Ridge National Laboratory’s leadership class supercomputer. The results presented in this work demonstrate temperature distribution smoothing in a nuclear reactor core through the manipulation of the geometry, which is traditionally achieved in light water reactors through variable assembly loading in the axial direction and fuel shuffling during refueling in the radial direction. The conclusions discuss the future implications for nuclear systems design with arbitrary geometry and the potential for AI-based autonomous design algorithms.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2013

Three-Axis Attitude Estimation With a High-Bandwidth Angular Rate Sensor

A continuing challenge for modern instrument pointing control systems is to meet the increasingly stringent pointing performance requirements imposed by emerging advanced scientific, defense, and civilian payloads. Instruments such as adaptive optics telescopes, space interferometers, and optical communications make unprecedented demands on precision pointing capabilities. A cost-effective method was developed for increasing the pointing performance for this class of NASA applications. The solution was to develop an attitude estimator that fuses star tracker and gyro measurements with a high-bandwidth angular rotation sensor (ARS). An ARS is a rate sensor whose bandwidth extends well beyond that of the gyro, typically up to 1,000 Hz or higher. The most promising ARS sensor technology is based on a magnetohydrodynamic concept, and has recently become available commercially. The key idea is that the sensor fusion of the star tracker, gyro, and ARS provides a high-bandwidth attitude estimate suitable for supporting pointing control with a fast-steering mirror or other type of tip/tilt correction for increased performance. The ARS is relatively inexpensive and can be bolted directly next to the gyro and star tracker on the spacecraft bus. The high-bandwidth attitude estimator fuses an ARS sensor with a standard three-axis suite comprised of a gyro and star tracker. The estimation architecture is based on a dual-complementary filter (DCF) structure. The DCF takes a frequency- weighted combination of the sensors such that each sensor is most heavily weighted in a frequency region where it has the lowest noise. An important property of the DCF is that it avoids the need to model disturbance torques in the filter mechanization. This is important because the disturbance torques are generally not known in applications. This property represents an advantage over the prior art because it overcomes a weakness of the Kalman filter that arises when fusing more than one rate measurement. An additional advantage over prior art is that, computationally, the DCF requires significantly fewer real-time calculations than a Kalman filter formulation. There are essentially two reasons for this: the DCF state is not augmented with angular rate, and measurement updates occur at the slower gyro rate instead of the faster ARS sampling rate. Finally, the DCF has a simple and compelling architecture. The DCF is exactly equivalent to flying two identical attitude observers, one at low rate and one at high rate. These attitude observers are exactly of the form currently flown on typical three-axis spacecraft.

Bayard, David S.↗
NASA NTRS2010

Reconciling Scientific Aspirations and Engineering Constraints for a Lunar Mission via Hyperdimensional Interpolation

Virtually every NASA space-exploration mission represents a compromise between the interests of two expert, dedicated, but very different communities: scientists, who want to go quickly to the places that interest them most and spend as much time there as possible conducting sophisticated experiments, and the engineers and designers charged with maximizing the probability that a given mission will be successful and cost-effective. Recent work at NASA's Jet Propulsion Laboratory (JPL) seeks to enhance communication between these two groups, and to help them reconcile their interests, by developing advanced modeling capabilities with which they can analyze the achievement of science goals and objectives against engineering design and operational constraints. The analyses conducted prior to this study have been point-design driven. Each analysis has been of one hypothetical case which addresses the question: Given a set of constraints, how much science can be done? But the constraints imposed by the architecture team-e.g., rover speed, time allowed for extravehicular activity (EVA), number of sites at which science experiments are to be conducted- are all in early development and carry a great deal of uncertainty. Variations can be incorporated into the analysis, and indeed that has been done in sensitivity studies designed to see which constraint variations have the greatest impact on results. But if a very large number of variations can be analyzed all at once, producing a table that includes virtually the entire trade space under consideration, then we have a tool that enables scientists and mission architects to ask the inverse question: For a given desired level of science (or any other objective), what is the range of constraints that would be needed? With this tool, mission architects could determine, for example, what combinations of rover speed, EVA duration, and other constraints produce the desired results. Further, this tool would help them identify which technology-improvement investments would be likely to produce the largest or most important return. However, the number of variations that need to be considered for such analysis quickly balloons to an unwieldy size. If three variations are considered for each of six constraints-a very modest example-there are a total of 243 variations to consider. If it takes 40 minutes to compute each variation, as it does with HURON, our automated optimization system, then it would take 162 hours or nearly 7 days of round-the-clock computing to calculate the results. Adding further constraints or variations exponentially increases the amount of time that is needed.

Weisbin, Charles R.↗
DOE OSTI2022

U.S. Department of Energy: National Virtual Biotechnology Laboratory (Technical Report)

With funding from the CARES Act, the U.S Department of Energy (DOE) established the National Virtual Biotechnology Laboratory (NVBL) in March 2020 to address key challenges associated with the COVID-19 crisis. The NVBL brought together the broad scientific and technical expertise and resources of DOE’s 17 national laboratories to help tackle medical supply shortages, discover potential drugs to fight the virus, develop and validate COVID-19 testing methods, model disease spread and impact across the nation, and understand virus transport in buildings and the environment. National laboratory resources leveraged for this effort include a suite of world-leading user facilities broadly available to the research community, such as light and neutron sources, nanoscale science research centers, sequencing and biocharacterization facilities, and high-performance computing facilities. As part of the NVBL framework, DOE rapidly assembled five project teams to (1) identify new targets for medical therapeutics; (2) develop innovations in testing capabilities; (3) provide epidemiological and logistical support; (4) understand viral fate and transport in the environment; and (5) address supply chain bottlenecks by harnessing extensive additive manufacturing capabilities. Each research team was charged with defining high-impact projects that could be completed in a 6-month sprint while coordinating their developments with academia, other government agencies, and the private sector. Within months, NVBL teams used DOE’s high-performance computers and light and neutron sources to identify promising candidates for antibodies and antivirals that universities and drug companies are now evaluating. NVBL researchers also developed new diagnostic targets and sample collection approaches, and supported efforts by the U.S. Food and Drug Administration, Centers for Disease Control and Prevention, and U.S. Department of Defense to establish national guidelines used in administering millions of tests. Researchers used artificial intelligence and high-performance computing to produce near-real-time data analysis to forecast disease transmission, stress on public health infrastructure, and economic impact, which supported decision-makers at the local, state, and national levels. To minimize virus uptake and protect human health, NVBL teams studied how to control indoor virus movement. Researchers also produced innovations in materials and advanced manufacturing that mitigated shortages in test kits and personal protective equipment, creating nearly 1,000 new jobs. Through its NVBL framework, DOE has contributed significantly to the nation’s COVID response, demonstrating in only a few months the critical impact of its national laboratories. NVBL’s accomplishments demonstrate not only the powerful resource represented by DOE’s national laboratories working together to meet national needs, but also the effectiveness of the integrated NVBL framework for rapidly responding to emergencies with research and development solutions. Going forward, the NVBL is poised to apply the unique capabilities and expertise of the national laboratory complex to future national and international emergencies, both natural and engineered. Through this framework, DOE will continue to be an integral component of agency-wide efforts to prepare for and respond to biorisks and other crises. This technical report describes the goals, progress, and results of NVBL’s five project teams—Molecular Design for COVID-19 Therapeutics, COVID-19 Testing, Epidemiological Modeling, Viral Fate and Transport, and Materials and Manufacturing of Critical Supplies—and lists each team’s publications and research output.

42 ENGINEERING↗
DOE OSTI2023

State of the Art of Molecular Visualization in Immersive Virtual Environments

Abstract Visualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head‐mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

How does the connectivity of open-framework conglomerates within multi-scale hierarchical fluvial architecture affect oil-sweep efficiency in waterflooding?

Understanding multi-phase fluid flow and transport processes within aquifers, candidate reservoirs for CO 2 sequestration, and petroleum reservoirs requires understanding a diverse set of geologic properties of the aquifer or reservoir, over a wide range of spatial and temporal scales. We focus on multiphase flow dynamics with wetting (e.g., water) and non-wetting (e.g., gas or oil) fluids, with one invading another. This problem is of general interest in a number of fields and is illustrated here by considering the sweep efficiency of oil during a waterflood. Using a relatively fine-resolution grid throughout a relatively large domain in these simulations and probing the results with advanced scientific visualization tools (Reservoir Visualization Analysis [RVA]/ ParaView software) promote a better understanding of how smaller-scale features affect the aggregate behavior at larger scales. We studied the effects on oil-sweep efficiency of the proportion, hierarchical organization, and connectivity of high-permeability open-framework conglomerate (OFC) cross-sets within the multi-scale stratal architecture found in fluvial deposits. We further analyzed oil production rate, water breakthrough time, and spatial and temporal distribution of residual oil saturation. As expected, the effective permeability of the reservoir exhibits large-scale anisotropy created by the organization of OFC cross-sets within unit bars, and the organization of unit bars within compound- bars. As a result, oil-sweep efficiency critically depends on the direction of the pressure gradient. However, contrary to expectations, the total amount of trapped oil due to the effect of capillary trapping does not depend on the magnitude of the pressure gradient within the examined range. Hence the pressure difference between production and injection wells does not affect sweep efficiency; although the spatial distribution of oil remaining in the reservoir depends on this value. Whether or not clusters of connected OFC span the domain affects only the absolute rate of oil production—not sweep efficiency.

58 GEOSCIENCES↗
DOE OSTI2015

The GRISM Lens-Amplified Survey from Space (GLASS). I. Survey overview and first data release

We give an overview of the Grism Lens Amplified Survey from Space (GLASS), a large Hubble Space Telescope program aimed at obtaining grism spectroscopy of the fields of 10 massive clusters of galaxies at redshift z = 0.308–0.686, including the Hubble Frontier Fields (HFF). The Wide Field Camera 3 (WFC3) yields near-infrared spectra of the cluster cores covering the wavelength range 0.81–1.69 μm through grisms G102 and G141, while the Advanced Camera for Surveys in parallel mode provides G800L spectra of the infall regions of the clusters. The WFC3 spectra are taken at two almost orthogonal position angles in order to minimize the effects of confusion. After summarizing the scientific drivers of GLASS, we describe the sample selection as well as the observing strategy and data processing pipeline. We then utilize MACS J0717.5+3745, a HFF cluster and the first one observed by GLASS, to illustrate the data quality and the high-level data products. Each spectrum brighter than H{sub AB}=23 is visually inspected by at least two co-authors and a redshift is measured when sufficient information is present in the spectra. Furthermore, we conducted a thorough search for emission lines through all of the GLASS WFC3 spectra with the aim of measuring redshifts for sources with continuum fainter than H{sub AB}=23. We provide a catalog of 139 emission-line-based spectroscopic redshifts for extragalactic sources, including three new redshifts of multiple image systems (one probable, two tentative). In addition to the data itself, we also release software tools that are helpful to navigate the data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Final Technical Report - Rapid Surface Microanalysis using a Low Temperature Plasma

This project focused on improving our current understanding and scientific knowledge in the area of plasma-surface interactions and plasma assisted material synthesis related to advanced microelectronics and nanotechnology. Current challenges include: controlling the interaction of Low Temperature Plasma (LTP) with a single layer of atoms to manufacture integrated circuits, continued miniaturization of integrated circuits, LTP processing of material surfaces and thin films to enable industrial scale fabrication of advanced microelectronics, synthesis of new materials, nanomaterials, nanotubes, and complex materials, Technology developed in this subtopic is of value to either (i) enable scans of surfaces (~1 sq. cm area) using various microscopies (electron, optical, other) at high resolution (micron or sub-micron resolution) rapidly (hours or days rather than years to complete a high-resolution scan of such a large surface area), or (ii) enable scans of surfaces (~1 sq. cm area) using various microscopies (electron, optical, other) at relatively low resolution rapidly, then apply algorithms to select spots for micron-scale imaging. Sputtering occurs when particles of a solid material are ejected from its surface by energetic particles from a plasma. While the degradation of the solid material and the subsequent deposition of the ejected material onto vulnerable surfaces are the usual subjects of sputtering studies, plasma science has yet to be combined with sputtering to create new diagnostics devices and systems. Small changes in the design of the plasma discharge device make it possible to create broad plasma beams for rapid scanning or small plasma beams to obtain the distribution of ejected elements with micron resolution. In the high-resolution use, the ion flux is extracted from the gas-discharge plasma and focused by a spherical emission surface to micron sizes onto the target specimen, providing very local sputtering and local elemental analysis. We call this “self-focusing”. The radiation from the excited and ionized sputtered atoms is recorded by a spectrometer through a window and fiberglass cable and analyzed with standard software packages used for optical glow discharge spectroscopy. Computer simulations of beam formation were used to verify and optimize the designs to be tested. A prototype was designed, constructed, and used to start experiments of beam formation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS2013

NASA Tech Briefs, April 2013

Topics covered include: Fully Integrated, Miniature, High-Frequency Flow Probe Utilizing MEMS Leadless SOI Technology; Nanoscale Surface Plasmonics Sensor With Nanofluidic Control; Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes; Neural Network Back-Propagation Algorithm for Sensing Hypergols; Bulk Moisture and Salinity Sensor; Change-Based Satellite Monitoring Using Broad Coverage and Targetable Sensing; Circularly Polarized Microwave Antenna Element with Very Low Off-Axis Cross-Polarization; Ultra-Low Heat-Leak, High-Temperature Superconducting Current Leads for Space Applications; Flash Cracking Reactor for Waste Plastic Processing; An Automated Safe-to-Mate (ASTM) Tester; Wireless Chalcogenide Nanoionic-Based Radio-Frequency Switch; Compute Element and Interface Box for the Hazard Detection System; DOT Transmit Module; Composite Aerogel Multifoil Protective Shielding; Li-Ion Electrolytes with Improved Safety and Tolerance to High-Voltage Systems; Polymer-Reinforced, Non-Brittle, Lightweight Cryogenic Insulation; Controlled, Site-Specific Functionalization of Carbon Nanotubes with Diazonium Salts; Regenerable Sorbent for CO2 Removal; Sprayable Aerogel Bead Compositions With High Shear Flow Resistance and High Thermal Insulation Value; Lexan Linear Shaped Charge Holder with Magnets and Backing Plate; Robotic Ankle for Omnidirectional Rock Anchors; Wind, Wave, and Tidal Energy Without Power Conditioning; An Active Heater Control Concept to Meet IXO Type Mirror Module Thermal-Structural Distortion Requirement; Waterless Clothes-Cleaning Machine; Integrated Electrical Wire Insulation Repair System; LVGEMS Time-of-Flight Mass Spectrometry on Satellites; Surface Inspection Tool for Optical Detection of Surface Defects; Per-Pixel, Dual-Counter Scheme for Optical Communications; Certification-Based Process Analysis; Surface Navigation Using Optimized Waypoints and Particle Swarm Optimization; Smart-Divert Powered Descent Guidance to Avoid the Backshell Landing Dispersion Ellipse; Estimating Foreign-Object-Debris Density from Photogrammetry Data; Adaptive Sampling of Spatiotemporal Phenomena with Optimization Criteria; Building a 2.5D Digital Elevation Model From 2D Imagery; Eyes on the Earth 3D; Target Trailing With Safe Navigation for Maritime Autonomous Surface Vehicles; Adams-Based Rover Terramechanics and Mobility Simulator - ARTEMIS; ISTP CDF Skeleton Editor; Uplink Summary Generator (ULSGEN) Version 1.0; Robotics On-Board Trainer (ROBoT); Software Engineering Tools for Scientific Models; Automatic Data Filter Customization Using a Genetic Algorithm; Tracker Toolkit; Towards Efficient Scientific Data Management Using Cloud Storage; On a Formal Tool for Reasoning About Flight Software Cost Analysis; A Nanostructured Composites Thermal Switch Controls Internal and External Short Circuit in Lithium Ion Batteries; Spacecraft Crew Cabin Condensation Control; and Functional Near-Infrared Spectroscopy Signals Measure Neuronal Activity in the Cortex.

Source record↗
DOE OSTI2021

The Role of Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology: Proceedings of a Workshop

Quantum concepts hold the potential to enable significant advances in sensing and imaging technologies that could be vital to the study of biological systems. The workshop Quantum Science Concepts in Enhancing Sensing and Imaging Technologies: Applications for Biology, held online March 8–10, 2021, was organized to examine the research and development needs to advance biological applications of quantum technology. Hosted by the National Academies of Sciences, Engineering, and Medicine, the event brought together experts working on state-of-the-art, quantum-enabled technologies and scientists who are interested in applying these technologies to biological systems. Through talks, panels, and discussions, the workshop facilitated a better understanding of the current and future biological applications of quantum-enabled technologies in fields such as microbiology, molecular biology, cell biology, plant science, mycology, and many others. The workshop was organized around three main themes. The first, quantum in biology, examined quantum concepts that are hypothesized to be important for life processes and that researchers are working to observe through biological imaging and sensing. The second, quantum for biology, addressed ways to use quantum concepts to enhance technologies for biological imaging and sensing. The third, biology for quantum, offered a wider discussion of how the frontiers of biological imaging and sensing could enable future study using quantum concepts, tools, or technologies. Throughout the workshop, participants identified a wide range of emerging approaches and opportunities at the intersection of quantum physics and biological sensing and imaging. During the workshop, there were some differences in how each speaker defined the term quantum. During one of the panels, Prem Kumar offered thoughts on what phenomena are classical versus quantum, explaining that techniques get progressively more quantum as you move from just having superposition to having superposition with measurement and entanglement. Another explanation from Clarice Aiello delineates the definition into several levels. This includes a base level of “quantum-ness,” which reflects that all matter is made of atoms, and when these particles are isolated they behave based on quantum mechanical principles. A second level is related to quantum coherence, where a single quantum object might be found in a coherent superposition state. A final level, which she described as the quantum-entangled level, involves multiple quantum systems which are entangled among themselves. Overall, the workshop touched on concepts such as superposition, entanglement, and squeezing and their potential implications for communication, computing, and simulation, in addition to the workshop’s main focal area, biological sensing and imaging. At the opening of the workshop, Thorsten Ritz of the University of California, Irvine, identified two questions at the heart of quantum biology: Is the machinery of life quantum mechanical, and can quantum mechanics be used to study the machinery of life in new ways? Participants highlighted systems in which researchers have explored these questions, from the vast array of molecular interactions involved in biological processes such as photosynthesis, to the mechanics involved in cellular functions such as differentiation and aggregation, to the role of oscillating magnetic fields in flight orientation among birds. Sensing and imaging technologies are crucial to biological research; these technologies could both enhance the study of quantum effects and be enhanced by quantum concepts. A critical challenge in biological research is to develop imaging and sensing tools that do not damage or interfere with the often fragile and fleeting systems being studied. Attendees discussed a variety of established and emerging technologies that could enhance noninvasive biological imaging, including single- and two-photon spectroscopy, single-molecule spectroscopy, quantum illumination, ghost imaging, and cryo-electron microscopy. One example came from Marlan Scully who gave a keynote address on the first day of the workshop. Scully emphasized the use of different laser technologies, which exhibit coherence and other quantum properties, in moving toward real-world biological applications, such as the detection of SARS-CoV-2. Both tools and theory will play an important role in advancing quantum biology research and applications. Several participants suggested theorists and experimentalists should work in tandem to understand and model biological processes. While physics often reduces systems to their simplest forms for fundamental insights, participants also noted the value of observing and understanding biological systems in all their “messiness,” capturing both the inner workings of biological systems and the complex interactions that occur within and between organisms. In discussions among participants, several attendees stressed the need to match emerging tools with the right scientific questions. Rather than developing quantum technologies as “a hammer looking for a nail,” participants emphasized a focus on exploring the problems these technologies are best suited to address. For example, it is important to consider the size of the phenomenon being studied, the timescales that are important in answering the scientific question, and other relevant considerations. Every tool along the spectrum from classical to quantum involves its own set of trade-offs. For example, Ted Laurence of the Lawrence Livermore National Laboratory said that quantum measurements, such as single photon counting, fluorescence transitions, and lasers, can take longer to produce the same results as classical measurements. These quantum measurements, however, do not require calibration and can enable new research questions to be answered. Prem Kumar, Northwestern University, noted that, despite their promise, quantum approaches should not be used simply for the sake of using quantum, especially in situations where classical approaches better meet the needs of the researcher. Understanding and applying quantum concepts could enable advances in a wide range of application areas including energy, synthetic biology, medicine, and sustainability. For example, Michelle O’Malley, University of California, Santa Barbara, described how improved noninvasive imaging approaches could help capture the complex interactions and functions involved in the breakdown of organic matter by microbial communities and lead to new technologies for capturing valuable products from plant waste. Several other participants discussed needs in tracking the movement of metabolites and molecules in microbial communities for insights into nutrient cycling in environments such as soil. Margaret Ahmad, Sorbonne University, discussed potential opportunities to leverage the magnetic properties of cryptochromes to advance new treatment approaches for diseases such as COVID-19 and cancer. Looking toward the future development of the field, participants discussed challenges to advancing quantum biology that arise from disciplinary disconnects between physicists and biologists. The siloing of academic research disciplines represents a significant barrier to progress. Disconnects in terminology, motivations and priorities, and structural barriers to collaborative work underscore the need for concerted efforts to bridge these divides. Attendees and speakers offered suggestions for resolving these divergences, establishing a shared language, moving the field forward, and fostering meaningful feedback between disciplines. Overall, participants stressed a need for balance, open communication, collaboration, unity, and clear dialogue on trade-offs between quantum and classical approaches. Keiko Torii, The University of Texas at Austin, said that the best collaborations happen when the project provides mutual advantages that can show off everyone’s talents, each team finds the work interesting, and partners develop a camaraderie to pursue new knowledge. To enable near- and long-term opportunities in this space, participants suggested that exploratory, high-risk funding could improve existing instrumentation to explore quantum enhancement collaboratively. They also emphasized the need for collaboration between quantum physicists and sensing/imaging scientists, which could be advanced through a dedicated quantum biology investigator program. People, even more than technology, will be crucial to the future of quantum biology. Participants explored training, education, and workforce needs to further develop this burgeoning field and cultivate the next generation of scientists. While many programs are still in their nascent stages, participants highlighted examples of approaches and programs being developed at various types of institutions to engage students and professional scientists in quantum biology research. Several participants stressed the need for an inclusive approach, spanning disciplines as well as communities to foster a diverse field fueled by the intellectual contributions of a wide range of people, including historically under-resourced schools and students. To increase awareness and excitement about quantum physics and related areas of biology, attendees suggested capitalizing on the “buzz” around quantum. Several participants emphasized the need to start early, introducing students to quantum concepts and their appealing “weirdness” in K–12 education. Engaging students early—before they become entrenched in traditional disciplinary siloes as typically happens in graduate school—could help to galvanize interest in the area and foster a generation of scientists with the interdisciplinary mindset and skills needed to advance this interdisciplinary field. While these efforts could be advanced at many levels and across multiple sectors, several participants suggested a national quantum biology center could be a valuable hub to coordinate and support quantum biology education and workforce development across academia, industry, and government.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes

Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Geometry-complete perceptron networks for 3D molecular graphs

Abstract Motivation The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional machine learning. Within this spirit, in this work, we introduce GCPNet, a new chirality-aware SE(3)-equivariant graph neural network designed for representation learning of 3D biomolecular graphs. We show that GCPNet, unlike previous representation learning methods for 3D biomolecules, is widely applicable to a variety of invariant or equivariant node-level, edge-level, and graph-level tasks on biomolecular structures while being able to (1) learn important chiral properties of 3D molecules and (2) detect external force fields. Results Across four distinct molecular-geometric tasks, we demonstrate that GCPNet’s predictions (1) for protein–ligand binding affinity achieve a statistically significant correlation of 0.608, more than 5%, greater than current state-of-the-art methods; (2) for protein structure ranking achieve statistically significant target-local and dataset-global correlations of 0.616 and 0.871, respectively; (3) for Newtownian many-body systems modeling achieve a task-averaged mean squared error less than 0.01, more than 15% better than current methods; and (4) for molecular chirality recognition achieve a state-of-the-art prediction accuracy of 98.7%, better than any other machine learning method to date. Availability and implementation The source code, data, and instructions to train new models or reproduce our results are freely available at https://github.com/BioinfoMachineLearning/GCPNet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Dilute Source CO 2 Capture: Management of Atmospheric Coal-Produced Legacy Emissions (Final Technical Report)

This report presents the scientific and technical findings by Carbon Engineering Ltd (CE) resulting from the DOE sponsored research and development project on Direct Air Capture (DAC) process. The work was performed by CE under the Award DE-FE0026861 from the US Department of Energy’s (DOE) National Energy Technology Laboratory (NETL). The work plan was centered on advancing the Recipient’s DAC technology, developing a better understanding of its performance and cost. The report outlines the results from two key activities that were performed under this award: Studying the performance characteristics of the DAC plant at both pilot and lab scale Techno-economic analysis (TEA) to study two implementations of CE’s proprietary DAC process: i) An early commercial DAC process that captures ~1 MT CO 2 /year of atmospheric CO 2 (Baseline case), and ii) A modified early commercial DAC process that captures 1 MT CO 2 /year of flue gas CO 2 downstream of a subcritical PC post-CCS power plant (Case 1 Option C). Within the first key activity of the project, significant operational improvements and learnings were realized, including for key equipment in the DAC process in addition to the overall system. Learnings included impact of equipment design on the capture, calcination and slaking processes, and operational parameter modifications for optimizing the pelletization process, along with the complex interactions and associated considerations of these on overall system performance. In parallel, benchtop scale equipment was successfully utilized to validate pilot scale data, such as, retention in the pellet reactor, and capture performance parameters of the air contactor. CE continues to conduct core research and development activities; the learnings from this DOE project will contribute to the design, testing and operation activities of CE’s fully integrated DAC validation plant, as well as the first commercial DAC plant. The second key activity incorporated the above learnings, as well as input from vendors and EPC experts, to produce the TEA report. More specifically, the TEA results were based on measured pilot plant performance data for all major unit operations, and on an energy and material balance for the process at commercial scale computed using an Aspen One® process simulation. The cost estimation methodology follows AACE International recommended practices; with an uncertainty range of -15%/+30%. Depending on financial assumptions, energy costs, and the specific choice of inputs and outputs, the levelized cost per ton CO 2 captured from the atmosphere for an early commercial plant for the baseline case ranges from 227 to 304 $/t-CO 2 . And, the levelized cost to capture 98% of the flue gas CO 2 emissions in a subcritical PC power plant (Case 1 Option C) through a combination of Cansolv carbon capture system and a modified DAC system was estimated to range from 109 to 160 $/MWh of net power generated. Process improvement opportunities identified during this work, in addition to cost reduction opportunities identified during the Value Engineering exercise associated with the Baseline case indicate that significant reductions in capital costs are available as CE scales up and proceeds through deployment of initial facilities.

20 FOSSIL-FUELED POWER PLANTS↗