Advances in machine learned potentials for molecular dynamics simulation
No abstract provided.
74 ATOMIC AND MOLECULAR PHYSICS↗
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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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w17_mnccatalysis: Investigation of Structure and Reactivity Relationship in M-N-C Type Catalysts using Density Functional Tight Binding
Abstract not provided.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Complexity-theoretic foundations of BosonSampling with a linear number of modes
BosonSampling is the leading candidate for demonstrating quantum computational advantage in photonic systems. While we have recently seen many impressive experimental demonstrations, there is still a formidable distance between the complexity-theoretic hardness arguments and current experiments. One of the largest gaps involves the ratio of {particles} to modes -- all current hardness evidence assumes a dilute regime in which the number of linear optical modes scales at least quadratically in the number of particles. By contrast, current experiments operate in a saturated regime with a linear number of modes. In this paper we bridge this gap, bringing the hardness evidence for experiments in the saturated regime to the same level as had been previously established for the dilute regime. This involves proving a new worst-to-average-case reduction for computing the Permanent which is robust to both large numbers of row repetitions and also to distributions over matrices with correlated entries. We also apply similar arguments to give evidence for hardness of Gaussian BosonSampling in the saturated regime.
Computational Complexity (cs.CC)↗
Pseudorandomness from Subset States
We show it is possible to obtain quantum pseudorandomness and pseudoentanglement from random subset states -- i.e. quantum states which are equal superpositions over (pseudo)random subsets of strings. This answers an open question of Aaronson et al. [arXiv:2211.00747], who devised a similar construction augmented by pseudorandom phases. Our result follows from a direct calculation of the trace distance between t copies of random subset states and the Haar measure, via the representation theory of the symmetric group. We show that the trace distance is negligibly small, as long as the subsets are of an appropriate size which is neither too big nor too small. In particular, we analyze the action of basis permutations on the symmetric subspace, and show that the largest component is described by the Johnson scheme: the double-cosets of the symmetric group S N by the subgroup S t x S N-t . The Gelfand pair property of this setting implies that the matrix eigenbasis coincides with the symmetric group irreducible blocks, with the largest eigenblock asymptotically approaching the Haar average. An immediate corollary of our result is that quantum pseudorandom and pseudoentangled state ensembles do not require relative phases.
Computational Complexity (cs.CC)↗
On the hardness of learning ground state entanglement of geometrically local Hamiltonians
Characterizing the entanglement structure of ground states of local Hamiltonians is a fundamental problem in quantum information. In this work we study the computational complexity of this problem, given the Hamiltonian as input. Our main result is that to show it is cryptographically hard to determine if the ground state of a geometrically local, polynomially gapped Hamiltonian on qudits (d=O(1)) has near-area law vs near-volume law entanglement. This improves prior work of Bouland et al. (arXiv:2311.12017) showing this for non-geometrically local Hamiltonians. In particular we show this problem is roughly factoring-hard in 1D, and LWE-hard in 2D. Our proof works by constructing a novel form of public-key pseudo-entanglement which is highly space-efficient, and combining this with a modification of Gottesman and Irani's quantum Turing machine to Hamiltonian construction. Our work suggests that the problem of learning so-called "gapless" quantum phases of matter might be intractable.
Computational Complexity (cs.CC)↗
Global Framework for Emulation of Nuclear Calculations
We introduce a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network, developing emulators capable of accurately predicting nuclear properties across isotopic chains simultaneously and being applicable to different regions of the nuclear chart. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii, while providing robust uncertainty quantification. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.
FOS: Computer and information sciences↗
Exploiting correlations in multi-coincidence Coulomb explosion patterns for differentiating molecular structures using machine learning
Coulomb explosion imaging (CEI) is a powerful technique for capturing the real-time motion of individual atoms during ultrafast photochemical reactions. CEI generates high-dimensional data with naturally embedded correlations that allow mapping the coordinated motion of nuclei in molecules. This enables reliable separation of competing reaction pathways and makes this approach uniquely suited for characterizing weak reaction channels. However, rich information contained in experimental CEI patterns remains largely underexploited due to challenges in visualizing correlations between multiple observables in multi-dimensional parameter space. Here we present a new approach to CEI of intermediate-sized polyatomic molecules, detecting up to eight ionic fragments in coincidence and leveraging machine-learning-based analysis to identify patterns and correlations in the resulting high-dimensional momentum-space data, enabling robust molecular structure identification and differentiation. Our approach provides high-dimensional background-free data encoding exceptionally rich structural information and establishes an automated, scalable framework for extracting insightful information from the data. As a demonstration, we apply this method to image and distinguish dichloroethylene isomers, showcasing its potential for broader applications in molecular imaging. Our results pave the way for channel-specific analysis of ultrafast structural dynamics in chemically relevant systems, particularly for disentangling mixed reaction pathways and detecting contributions from weak channels and minority species.
Chemical Physics (physics.chem-ph)↗
Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark -- A Bayesian Inverse UQ-based Approach for Data Assimilation
The Organization for Economic Cooperation and Development (OECD) Working Party on Nuclear Criticality Safety (WPNCS) proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian Inverse Uncertainty Quantification (IUQ) as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of Generalized Linear Least Squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. When comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that GLLS predictions fail to replicate computed response distributions for nonlinear applications, while MOCABA shows near agreement, and IUQ uses computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.
FOS: Computer and information sciences↗
hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware
We present hls4ml, a free and open-source platform that translates machine learning (ML) models from modern deep learning frameworks into high-level synthesis (HLS) code that can be integrated into full designs for field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). With its flexible and modular design, hls4ml supports a large number of deep learning frameworks and can target HLS compilers from several vendors, including Vitis HLS, Intel oneAPI and Catapult HLS. Together with a wider eco-system for software-hardware co-design, hls4ml has enabled the acceleration of ML inference in a wide range of commercial and scientific applications where low latency, resource usage, and power consumption are critical. In this paper, we describe the structure and functionality of the hls4ml platform. The overarching design considerations for the generated HLS code are discussed, together with selected performance results.
FOS: Computer and information sciences↗
Stochastic tensor contraction for quantum chemistry
Many computational methods in ab initio quantum chemistry are formulated in terms of high-order tensor contractions, whose cost determines the size of system that can be studied. We introduce stochastic tensor contraction to perform such operations with greatly reduced cost, and present its application to the gold-standard quantum chemistry method, coupled cluster theory with up to perturbative triples. For total energy errors more stringent than chemical accuracy, we reduce the computational scaling to that of mean-field theory, while starting to approach the mean-field absolute cost, thereby challenging the existing cost-to-accuracy landscape. Benchmarks against state-of-the-art local correlation approximations further show that we achieve an order-of-magnitude improvement in both total computation time and error, with significantly reduced sensitivity to system dimensionality and electron delocalization. We conclude that stochastic tensor contraction is a powerful computational primitive to accelerate a wide range of quantum chemistry.
Chemical Physics (physics.chem-ph)↗
CELSS science needs
Questions and areas of study that need to be persued in order to develope a Controlled Ecological Life Support System are posed. Research topics needing attention are grouped under various leadings: ecology, genetics, plant pathology, cybernetics, chemistry, computer science, fluid dynamics, optics, and solid-state physics.
Rummel, J. D.↗
Support of Integrated Health Management (IHM) through Automated Analyses of Flowfield-Derived Spectrographic Data
Flow-field analysis techniques under continuing development at NASA's Marshall Space Flight Center are the foundation for a new type of health monitoring instrumentation for propulsion systems and a vast range of other applications. Physics, spectroscopy, mechanics, optics, and cutting-edge computer sciences merge to make recent developments in such instrumentation possible. Issues encountered in adaptation of such a system to future space vehicles, or retrofit in existing hardware, are central to the work. This paper is an overview of the collaborative efforts results, current efforts, and future plans.
Patrick, Marshall C.↗
The X-43A Flight Research Program: Lessons Learned on the Road to Mach 10
During an aerospace engineer's undergraduate studies, he or she will attend classes in aerodynamics, thermodynamics, structures, stability and control, dynamics, design, propulsion, and computer science, along with the related courses in mathematics, physics, statistics, and chemistry required to understand the material. Upon graduation, the new engineer will have acquired a basic knowledge of how to build an aerospace vehicle. What only comes through experience, however, is the understanding of the inevitable imperfect process through which an aerospace vehicle is built. This is the adventure of turning a basic concept into functional hardware. Engineers working on a project must often deal with ambiguous situations. They are routinely asked by management to provide risk assessments of a project, yet even after careful analysis uncertainties remain. The project must be accomplished within finite limits of time and money. The question an engineer answers is whether the solution to potential problem is worth the cost and schedule delay, or if the solution might actually be worse than the problem it is meant to solve. Review protocols are established to ensure that an unknown has not been overlooked. But these cannot protect against an unknown unknown. Examples of these situations can be found in the history of the X-43A Hyper-X (Hypersonic Experiment) program. In this NASA project, a supersonic combustion ramjet (scramjet) engine was flight tested on a subscale vehicle. The X-43A Hyper-X Research Vehicle (HXRV) was launched from a B-52B mothership, then boosted to the test speed by a modified Pegasus rocket first stage, called the Hyper-X Launch Vehicle (HXLV). Once at the proper speed and altitude, the X-43A separated from the booster, stabilized itself, and then the engine test began. Although wind-tunnel scramjet engine tests had begun in the late 1950s, before the Hyper-X program there had never been an actual in-flight test of such an engine integrated with an appropriate airframe. Thus, while the scramjet had successfully operated in the artificial airflow of wind tunnels, the concept had yet to be proven in real air. These conditions meant changes in density and temperature, as well as changes in angle of attack and sideslip of a free-flying vehicle. A wind tunnel is limited in its ability to simulate these subtle factures, which have a major impact on almost any vehicle, but especially that of a scramjet's performance. The Hyper-X project was to provide a real-world benchmark of the ground test data. The full scale X-43A engine would be operated in the wind tunnel, and then flown, and the data from its operation would then be compared with projections. If these matched, the wind tunnel data would be considered a reliable design tool for future scramjet. If there were significant differences, the reasons for these would have to be identified. Until such information was available, scramjets would lack the technological maturity to be considered for future space launch or high-speed atmospheric flight vehicles.
Peebles, Curtis↗
Cumulative Index to NASA Tech Briefs 1963-1969
Tech Briefs are short announcements of new technology derived from the research and development activities of the National Aeronautics and Space Administration. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This Cumulative index to NASA Tech Briefs lists those published from 1963 through 1969. The main listing is divided into six categokies: Electrical (Electronic), Physical Sciences (Energy Sources), Materials (Chemistry), Life Sciences, Mechanical, and Computer Programs.
Source record↗
Cumulative Index to NASA Tech Briefs
Tech Briefs are short announcements of new technology derived from the R&D activities of the National Aeronautics and Space Administration. This Index to NASA Tech Briefs lists the technological innovations derived from the U.S. space program and published during the period January through December 1968. A new five year cycle of cumulative indexes begins with this index. The main section is arranged in six categories: Electrical (Electronic); Physical Sciences (Energy Sources); Materials (Chemistry); Life Sciences; Mechanical; and Computer Programs.
Source record↗
NASA Tech Briefs, February 2002
Topics include:a technology focus on computers, electronic components and systems, software, materials, mechanics,physical sciences machinery, manufacturing/fabrication, mathematics, book and reports, motion control tech briefs and a special section on Photonics Tech Briefs.
Source record↗
Proceedings of Synthetic Biology: Engineering, Evolution and Design (SEED) Conference 2015
Synthetic Biology is an emerging discipline that seeks to accelerate the process of engineering biology. As such, the tools are broadly applicable to application areas, including chemicals and biofuels, materials, medicine and agriculture. A characteristic of the field is to look holistically at cellular design, from sensing and genetic circuitry to the manipulation of cellular processes and actuators, to controlling metabolism, to programming multicellular behaviors. Further, the types of cells that are manipulated are broad, from in vitro systems to microbes and fungi to mammalian and plant cells and living animals. Many of the projects in synthetic biology seek to move biochemical functions across organisms. The field is highly interdisciplinary with faculty and students spread across departments that focus on engineering (biological, chemical, electrical, mechanical, civil, computer science) and basic science (biology and systems biology, chemistry, physics). While there have been many one-off workshops and meeting on synthetic biology, the 2014 Synthetic Biology: Engineering, Evolution and Design (SEED) was the first of an annual conference series that serves as a reliable place to pull together the involved disciplines in order to organize and exchange advances in the science and technology in the field. Further, the SEED conferences have a strong focus on industry, with many companies represented and actively participating. A number of these companies have started major efforts in synthetic biology including large companies (e.g., Pfizer, Novartis, Dow, Dupont, BP, Total), smaller companies have recently gone public (e.g., Amyris, Gevo, Intrexon), and many start-ups (e.g., Teslagen, Refactored Materials, Pivot, Genomatica). There are a number of loosely affiliated Synthetic Biology Centers, including ones at MIT, Boston University, UCSD, UCSF, UC-Berkeley, Imperial College, Oxford, and ETH. SEED 2015 will serve as the primary meeting at which international synthetic biology centers and related infrastructure (synthesis/software/foundries) meet to discuss technology, standards, and education. SEED2015 will be the second in an annual series of meeting held to bring researchers from industry and academia in the area of Synthetic Biology. The first SEED conference was highly successful, attracting 285 attendees with varying backgrounds from academia, industry and government. The SEED series provides leadership in the development of the field of synthetic biology and serves to broaden the participants in the field by appealing to broad sectors in industry and providing a means for young investigators and those outside of the field to participate. Further, the series closely integrates with groups such as the SBCC to provide a means by which the synthetic biology community can communicate with policy makers. Further, we will pursue making the meeting the center for the exchange of educational materials as centers for synthetic biology emerge globally. Proceedings will be published each year in the journal ACS Synthetic Biology. After each SEED meeting, surveys are distributed to assess the success of the conference and to help guide changes year-to-year. The diverse application areas further extend the expertise needed from people in areas such as plant biology, agriculture and soil science, environmental science, medicine, and the chemical industry. These areas could have a widespread impact on society in a number of ways. For example, the CRISPR/Cas9 system that serves to immunize bacteria from phage has provided the fundamental chemistry that is used to edit the genomes of diverse organisms, including human stem cells, crop plants, and livestock animals.
09 BIOMASS FUELS↗