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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Quantum Machine Learning

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Biswas, Rupak↗
DOE OSTI2024

Acceleration of uranium beam to record power of 10.4 kW and observation of new isotopes at Facility for Rare Isotope Beams

The Facility for Rare Isotope Beams (FRIB) is a major nuclear physics facility for research with fast, stopped, and reaccelerated beams that was successfully commissioned in May 2022. A key capability of FRIB is the production of an acceleration of the uranium beam, but this capability requires the facility to work at the design limits of the lowest charge-to-mass ratio and the highest power density on the beam intercepting devices. This paper presents techniques for overcoming the significant challenges in accelerating the uranium beam, culminating in the demonstration of 10.4 kW on target, and the discovery of three new isotopes. The high-power uranium beam enabled us to produce and identify G 88 a , A 93 s , and S 96 e , within the first 24 h of operation. The successful uranium operation at FRIB sets a new record for accelerated uranium beam power above 10 kW and opens a new avenue of research with rare isotopes. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

The Fermilab Facility for Dark Matter Discovery (F2D2): A Conceptual PIP-II Beam Stop Facility for Dark Sector Physics

What do the accelerator target stations of the next decade look like? The NuMI beamline, fed by Fermilab s Main Injector, recently exceeded 1 MW beam power. Future experiments fed by the PIP-II superconducting linear accelerator might demand upwards of 2 MW in continuous-wave mode, compared to the pulsed beams typical of neutrino experiments. Looking further into the future, the Muon Collider front end includes a target station with even higher power 5 MW or beyond. How do we build a 2+ MW target facility? How does a high-power, low-energy, CW beam affect target design compared to conventional high-energy neutrino targets? How do we manage radiation and heat? How is the facility serviced? What is the current state of capability, and what technologies do we need to develop? These questions are answered in the context of F2D2, a conceptual target station design for dark sector physics experiments using beam from PIP-II.

Williams, Jonathan K.↗
NASA NTRS2012

Hubble Space Telescope - Scientific, Technological and Social Contributions to the Public Discourse on Science

The Hubble Space Telescope has unified the world with a sense of awe and wonder for 2 I years and is currently more scientifically powerful than ever. I will present highlights of discoveries made with the Hubble Space Telescope, including details of planetary weather, star formation, extra-solar planets, colliding galaxies, and a universe expanding with the acceleration of dark energy. I will also present the unique technical challenges and triumphs of this phenomenal observatory, and discuss how our discoveries in the cosmos affect our sense of human unity, significance, and wonder.

Wiseman, Jennifer↗
DOE OSTI2023

Adaptive language model training for molecular design

Abstract The vast size of chemical space necessitates computational approaches to automate and accelerate the design of molecular sequences to guide experimental efforts for drug discovery. Genetic algorithms provide a useful framework to incrementally generate molecules by applying mutations to known chemical structures. Recently, masked language models have been applied to automate the mutation process by leveraging large compound libraries to learn commonly occurring chemical sequences (i.e., using tokenization) and predict rearrangements (i.e., using mask prediction). Here, we consider how language models can be adapted to improve molecule generation for different optimization tasks. We use two different generation strategies for comparison, fixed and adaptive. The fixed strategy uses a pre-trained model to generate mutations; the adaptive strategy trains the language model on each new generation of molecules selected for target properties during optimization. Our results show that the adaptive strategy allows the language model to more closely fit the distribution of molecules in the population. Therefore, for enhanced fitness optimization, we suggest the use of the fixed strategy during an initial phase followed by the use of the adaptive strategy. We demonstrate the impact of adaptive training by searching for molecules that optimize both heuristic metrics, drug-likeness and synthesizability, as well as predicted protein binding affinity from a surrogate model. Our results show that the adaptive strategy provides a significant improvement in fitness optimization compared to the fixed pre-trained model, empowering the application of language models to molecular design tasks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Physics in the Machine: Integrating Physical Knowledge in Autonomous Phase-Mapping

Application of artificial intelligence (AI), and more specifically machine learning, to the physical sciences has expanded significantly over the past decades. In particular, science-informed AI, also known as scientific AI or inductive bias AI, has grown from a focus on data analysis to now controlling experiment design, simulation, execution and analysis in closed-loop autonomous systems. The CAMEO (closed-loop autonomous materials exploration and optimization) algorithm employs scientific AI to address two tasks: learning a material system’s composition-structure relationship and identifying materials compositions with optimal functional properties. By integrating these, accelerated materials screening across compositional phase diagrams was demonstrated, resulting in the discovery of a best-in-class phase change memory material. Key to this success is the ability to guide subsequent measurements to maximize knowledge of the composition-structure relationship, or phase map. In this work we investigate the benefits of incorporating varying levels of prior physical knowledge into CAMEO’s autonomous phase-mapping. This includes the use of ab-initio phase boundary data from the AFLOW repositories, which has been shown to optimize CAMEO’s search when used as a prior.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

The Beginning and End of the Universe

Cosmology is the scientific study of how the Universe began more than 13 billion years ago, how its properties have changed from that time to the present, and what its eventual fate might be. Observational cosmology uses telescopes like the Hubble to reach back in time to find the faint echoes of the Big Bang. In this lecture, I will give an overview of cosmology, highlighting the very rapid progress this field has made in the last decade, and the role that NASA space telescopes have played and will continue to play in the years to come. I will then focus on two of the most intriguing of those recent discoveries: inflation and dark energy. Our universe began in an extremely rapid accelerated expansion, called inflation, which removed all traces anything that may have existed before, flattened the geometry of space-time, and turned microscopic quantum fluctuations into the largest structures in the universe. At the present time, more than 70% of the mass-energy in the Universe consists of a mysterious substance called dark energy. The dark energy causes the expansion of the Universe to accelerate, and he will discuss the ways that we might be able to measure that acceleration more accurately, revealing the nature of the dark energy and learning the eventual fate of the Universe.

Gardner, Jonathan P.↗
NASA NTRS2012

The Beginning and End of the Universe

Cosmology is the scientific study of how the Universe began more than 13 billion years ago, how its properties have changed from that time to the present, and what its eventual fate might be. Observational cosmology uses telescopes like the Hubble to reach back in time to find the faint echoes of the Big Bang. In this lecture, I will give an overview of cosmology, highlighting the very rapid progress this field has made in the last decade, and the role that NASA space telescopes have played and will continue to play in the years to come. I will then focus on two of the most intriguing of those recent discoveries: inflation and dark energy. Our universe began in an extremely rapid accelerated expansion, called inflation, which removed all traces anything that may have existed before, flattened the geometry of space-time, and turned microscopic quantum fluctuations into the largest structures in the universe. At the present time, more than 70% of the mass-energy in the Universe consists of a mysterious substance called dark energy. The dark energy causes the expansion of the Universe to accelerate, and he will discuss the ways that we might be able to measure that acceleration more accurately, revealing the nature of the dark energy and learning the eventual fate of the Universe.

Gardner, Jonathan P.↗
NASA NTRSDate not supplied

Attitude Control System for the Solar Cruiser and Future Solar Sail Missions

NASA’s Marshall Space Flight Center (MSFC) is developing the Solar Cruiser solar sail mission to continue to mature solar sail propulsion. Solar Cruiser is a 95kg spacecraft capable of deploying a 1654 square meter solar sail. Solar Cruiser will demonstrate science observation capabilities and navigation in sub Lagrange Point (L1) halo-orbits. Solar Cruiser sail technology can be scaled to larger solar sail missions, including attitude control system and sail membrane and boom technology. Larger solar sails could be used to image the solar poles, enabling Solar Polar Imager (SPI) missions currently prohibitively expensive for propellant-based propulsion methods. Similarly, solar sails can be used to create artificial equilibria and indefinite station-keeping at locations sunward of Lagrange Point one, L1, along the Sun-Earth line (SEL), which can provide space-weather monitor and prediction and reveal discoveries about our Sun and solar system. To achieve the high characteristic acceleration required for larger solar sail missions such as SPI, a solar sail area of approximately 7000 square-meters would be needed. In comparison, the largest solar sail flown to date is the JAXA’s IKAROS mission with a 196 square-meters sail. Attitude control an SPI-size sail poses challenges, including station keeping under solar sail induced torques and thrust vector pointing. In this study, the scalability of the Solar Cruiser sail attitude control architecture to larger sails is studied, such as pointing control performance, mass and power requirements. Solar Cruiser attitude control actuators include propellant-based, such as ion engines, and propellantless options which create differential solar pressures to generate spacecraft torques. Propellantless attitude control includes active translation of the center of mass, Reflectivity Control Devices (RCDs), and tip vanes. The study summarizes the scalability characteristics of the different attitude control architectures for larger solar sail missions.

Solar Sail Propulsion↗
DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗
DOE OSTI2022

Future Collider Options for the US

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

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Scaling SQL to the Supercomputer for Interactive Analysis of Simulation Data

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

Glaser, Jens↗
DOE OSTI2023

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

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

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

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

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

36 MATERIALS SCIENCE↗
DOE OSTI2022

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

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

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

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

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

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

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

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

36 MATERIALS SCIENCE↗
DOE OSTI2023

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

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

42 ENGINEERING↗