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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Decoding Active Sites for Highly Efficient Semihydrogenation of Acetylene in Palladium–Copper Nanoalloys

Accurately decoding the three-dimensional atomic structure of surface active sites is essential yet challenging for a rational catalyst design. Here, we used comprehensive techniques combining the pair distribution function and reverse Monte Carlo simulation to reveal the surficial distribution of Pd active sites and adjacent coordination environment in palladium-copper nanoalloys. After the fine-tuning of the atomic arrangement, excellent catalytic performance with 98% ethylene selectivity at complete acetylene conversion was obtained in the Pd 34 Cu 66 nanocatalysts, outperforming most of the reported advanced catalysts. Further, t he quantitative deciphering shows a large number of active sites with a Pd-Pd coordination number of 3 distributed on the surface of Pd 34 Cu 66 nanoalloys, which play a decisive role in highly efficient semihydrogenation. This finding not only opens the way for guiding the precise design of bimetal nanocatalysts from atomic-level insight but also provides a method to resolve the spatial structure of active sites.

36 MATERIALS SCIENCE↗
NASA NTRS2012

Progress in the Fabrication and Testing of Telescope Mirrors for the James Webb Space Telescope

The telescope of the James Webb Space Telescope (JWST) is an f/20, three mirror anastigmat design, passively cooled (40K) in an L2 orbit. The design provides diffraction limited performance (Strehl > or = 0.8) at .=2 m. To fit within the launch vehicle envelope (Arianne V), the 6.6 meter primary mirror and the secondary mirror support structure are folded for launch, then deployed and aligned in space. The primary mirror is composed of 18 individual, 1.3 meter (flat flat) hexagonal segments, each adjustable in seven degrees of freedom (six rigid body + radius of curvature) provided by a set of high precision actuators. The actuated secondary mirror (approx.0.74m) is similarly positioned in six degrees of rigid body motion. The approx..70x.51m, fixed tertiary and approx. 0.17m, flat fine steering mirror complete the telescope mirror complement. The telescope is supported by a composite structure optimized for performance at cryogenic temperatures. All telescope mirrors are made of Be with substantial light-weighting (21 kg for each 1.3M primary segment). Additional Be mounting and supporting structure for the high precision (approx.10nm steps) actuators are attached to the primary segments and secondary mirror. All mirrors undergo a process of thermal stabilization to reduce stress. An extensive series of interferometric measurements guide each step of the polishing process. Final polishing must account for any deformation between the ambient temperature of polishing and the cryogenic, operational temperature. This is accomplished by producing highly precise, cryo deformation target maps of each surface which are incorporated into the final polishing cycle. All flight mirrors have now completed polishing, coating with protected Au and final cryo testing, and the telescope is on track to meet all system requirements. We here review the measured performance of the component mirrors and the predicted performance of the flight telescope.

Bowers, Charles↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
NASA NTRS1992

Astrometric performance characteristics of the Hubble Space Telescope fine guidance sensors

Each of the three fine guidance sensors for the Hubble Space Telescope constitutes a sixth science instrument to be used for astrometry. We detail the tests and results used in choosing one of the three sensors to be the prime astrometer. The Astrometry Science Team has chosen Fine Guidance Sensor (FGS) 3. FGS 3 produces position measurements on a star with V = 17 with a peraxis precision of 0.003 arcsec. The interferometer response function should permit double-star astrometry at least down to V = 16 for the central region of FGS 3. In a 10-h test of the stability of POS-mode astrometric measurements made in FGS 3, we found no scale or orientation variations greater than two parts in 100,000. During this same time period, we found no statistically significant systematic guide-star radial separation changes. Spacecraft jitter is found to be the prime determinant of astrometry success or failure.

Benedict, G. F.↗
DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Abstract for CRADA between Mati Car bon PBC and NETL

The National Energy Technology Laboratory (NETL) will assist Mati Carbon PBC (participant) in the design and execution of their community benefits plan (CBP) for scaling Enhanced Rock Weathering (ERW) carbon dioxide removal (CDR) in Arkansas. ERW involves spreading finely crushed rocks in agricultural fields to speed up the dissolution and release of certain minerals that amend soil acidity, replenish soil nutrients, and simultaneously converting CO 2 from the atmosphere to Dissolved Inorganic Carbon (DIC). ERW not only removes CO 2 from the atmosphere but also has the potential to generate co-benefits for famers through cost reductions and increased crop yields. The collaboration between NETL and Mati Carbon, PBC (participant) will entail a detailed review of the economic, environmental and community impact of ERW in agricultural settings. This review will guide all project related decisions. It will provide a better understanding of the scale of these potential co-benefits and quantify community needs that ERW can address.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Search for Feebly Interacting Particles

At the dawn of a new decade, particle physics faces the challenge of explaining the mystery of dark matter, the origin of matter over antimatter in the Universe, the apparent fine-tuning of the electroweak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves New Physics at mass scales comparable to that of familiar matter—below the GeV scale but with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and existing data may even provide hints of this possibility. Emboldened by the lessons of the LHC, a vibrant experimental program to discover such physics is underway, guided by a systematic theoretical approach that is firmly grounded in the underlying principles of the Standard Model. We give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs, and we focus in particular on accelerator-based experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1992

Acquisition, pointing and tracking performance of the Hubble Space Telescope fine guidance sensors

The three fine guidance sensors (FGSs) on board the HST have been operated extensively since the observatory was launched in April, 1990. The FGSs, each capable of measuring angles as small as 0.003 arcsec, provide required fine pointing information to the HST's pointing control system and are intended to serve as astrometry instruments. On-orbit data have shown that the acquisition, pointing and tracking performance of the FGSs in most cases meets, and of these sometimes exceeds, requirements. The versatility of the FGS digital control electronics to adapt to the unexpected conditions imposed on the sensors by the telescope spherical aberration and by solar panel jitter is discussed. There is encouragement from both on-orbit tests and analytical studies that the FGSs can accommodate the current telescope characteristics. Improvements to guide star acquisitions within the FGSs and to target acquisitions within science instruments have been accomplished with the internal distortion calibration of each FGS and with the alignment calibration between sensors. Techniques used in the calibration process and the resulting improvements in acquisitions are presented.

Eaton, David J.↗
DOE OSTI2023

Feebly-interacting particles: FIPs 2022 Workshop Report

Particle physics today faces the challenge of explaining the mystery of dark matter, the origin of matter over anti-matter in the Universe, the origin of the neutrino masses, the apparent fine-tuning of the electro-weak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves new physics at mass scales comparable to familiar matter, below the GeV-scale, or even radically below, down to sub-eV scales, and with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and indeed, existing data provide numerous hints for such possibility. A vibrant experimental program to discover such physics is under way, guided by a systematic theoretical approach firmly grounded on the underlying principles of the Standard Model. This document represents the report of the FIPs 2022 workshop, held at CERN between the 17 and 21 October 2022 and aims to give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Feebly-Interacting Particles: FIPs 2022 Workshop Report

Particle physics today faces the challenge of explaining the mystery of dark matter, the origin of matter over anti-matter in the Universe, the origin of the neutrino masses, the apparent fine-tuning of the electro-weak scale, and many other aspects of fundamental physics. Perhaps the most striking frontier to emerge in the search for answers involves new physics at mass scales comparable to familiar matter, below the GeV-scale, or even radically below, down to sub-eV scales, and with very feeble interaction strength. New theoretical ideas to address dark matter and other fundamental questions predict such feebly interacting particles (FIPs) at these scales, and indeed, existing data provide numerous hints for such possibility. A vibrant experimental program to discover such physics is under way, guided by a systematic theoretical approach firmly grounded on the underlying principles of the Standard Model. This document represents the report of the FIPs 2022 workshop, held at CERN between the 17 and 21 October 2022 and aims to give an overview of these efforts, their motivations, and the decadal goals that animate the community involved in the search for FIPs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

AI‐Driven Robot Enables Synthesis‐Property Relation Prediction for Metal Halide Perovskites in Humid Atmosphere

Materials Acceleration Platforms (MAPs) – also known as self-driving laboratories– present a new paradigm for materials science and promise an order of magnitude accelerated materials discovery compared to the traditional trial-and-error approach. Metal halide perovskites (MHPs) are an emerging class of materials for optoelectronic applications but are plagued by irreproducible optoelectronic quality, particularly for films fabricated in a humid atmosphere. Here, in this work, a machine learning (ML)-guided closed-loop platform is developed with a multimodal data fusion approach to predict synthesis–property relations for the optical quality of MHP thin films in relative humidities (RHs) ranging from 5–55%. The efficiency of this approach is confirmed by the fast-dropping learning rate to 2% after experimentally sampling less than 1% of the possible 5,000+ combinations. The prediction of synthesis–property relations is done by optical and imaging characterizations. In situ photoluminescence characterization revealed the origin of thin film quality variation at different RH. These insights provide an avenue for controlling the MHP crystallization by fine-tuning the synthesis parameters and RH for a given chemistry, thus lifting the need for stringent atmosphere control. The MAP enables an accelerated screening and understanding of the synthesis design space, facilitating rational synthesis recipe choice for a wide range of materials.

AI-driven robot↗
NASA NTRS1977

Mass drivers. 1: Electrical design

A mass driver is an electrical device used to accelerate payloads of any material to a high velocity. Small vehicles (called buckets) containing superconducting coils carry the payloads. These buckets are accelerated by pulsed magnetic fields, timed by information on their position, and are guided by induced magnetic fields set up in a surrounding guideway. Upon reaching the correct velocity, the buckets release their payloads, then they are slowed for recirculation to be reused. A rationale is presented that leads to a relatively simple and near-optimum design, as well as basic programs for calculating acceleration. The transverse oscillation frequencies are found to be invariant to guideway transverse dimensions.

William H. Arnold↗
DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗
NASA NTRS1977

Mass drivers. 3: Engineering

This paper is the last of a series of three by the Mass-Driver Group of the 1977 Ames Summer Study. It develops the engineering principles required to implement the basic mass-driver design defined in paper I and as presented previously in the literature. Optimum component mass trade-offs are derived from a set of four input parameters, and the program used to design a lunar launcher. The mass optimization procedure is then incorporated into a more comprehensive mission optimization program called OPT-4, which evaluates an optimized mass-driver reaction engine and its performance in a range of specified missions. Finally, this paper discusses, to the extent that time permitted, certain peripheral problems: heating effects in buckets due to magnetic field ripple; an approximate derivation of guide force profiles; the mechanics of inserting and releasing payloads; the reaction mass orbits; and a proposed research and development plan for implementing mass drivers. This paper also considers further optimization that depends on advances in component technology beyond the present state. It is concluded that significant advantage can be gained in the case of small reaction engines with high emission velocity by developing lightweight packaging technology for silicon-controlled rectifiers (SCR's).

William H. Arnold↗
DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗