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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data

The ongoing growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Through access to a large number of time series data sets from different power plants, we have found common issues that impede the modeling process. Namely, the time series data are hard to transfer between groups due to differences in variable nomenclature, and the quality of the data sets can vary. We address the issue of variable nomenclature by FAIRifying spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. We have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. With access to large time series data sets across many power plants, we can utilize spatiotemporal coherence between the sites in order to improve the quality of our data. To solve the issue of data missingness, we propose the use of Spatiotemporal-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.

14 SOLAR ENERGY↗
NASA NTRS2004

Kinetic Roughening and Energetics of Tetragonal Lysozyme Crystal Growth: A Preliminary Atomic Force Microscopy Investigation

We examined particulars of crystal growth from measurements obtained at both microscopic and molecular levels. The crystal growth measurements performed at the microscopic level are well characterized by a model that balances the flux of macromolecules towards the crystal surface with the flux of the crystal surface. Numerical evaluation of model with measurements of crystal growth, in time, provided accurate estimates for the average growth velocities. Growth velocities thus obtained were also interpreted using well-established phenomenological theories. Moreover, we find that microscopic measurements of growth velocity measurements obtained as a function of temperature best characterizes changes in crystal growth modes, when present. We also examined the possibility of detecting a change in crystal growth modes at the molecular level using atomic force microscopy, AFM. From preliminary AFM measurements performed at various supersaturations, we find that magnitude of surface height fluctuations, h(x), increases with supersaturation. Further examination of surface height fluctuations using methods established for fluctuation spectroscopy also enabled the discovery of the existence of a characteristic length, c, which may possibly determine the mode of crystal growth. Although the results are preliminary, we establish the non- critical divergence of 5 and the root-mean-square (rms) magnitude of height-height fluctuations as the kinetic roughening transition temperatures are approached. Moreover, we also examine approximate models for interpreting the non-critical behavior of both 6 and rms magnitude of height-height fluctuations, as the solution supersaturation is increased towards the kinetic roughening supersaturation.

Gorti, Sridhar↗
NASA NTRS1980

Models for galactic X-ray sources

Attention is given to those compact galactic X-ray sources whose X-ray luminosities are considerably in excess of the solar luminosity. It is pointed out that the key breakthrough in the development of an understanding of compact galactic X-ray sources was the discovery of X-ray pulsars with the UHURU satellite. There is now overwhelming evidence that these objects are neutron stars in close binary stellar systems. The X-ray pulsations are thought to be thermal emission from the magnetic polar caps of a neutron star that is accreting matter from a companion star and whose magnetic field is misaligned with its rotation axis. Among the compact galactic X-ray sources that are not X-ray pulsars, some still show direct evidence of binary membership, such as X-ray eclipses. There is evidence that the galactic-bulge sources are, in fact, close binary stellar systems. It is concluded, that the great majority of bright galactic X-ray sources, with only a tiny handful of exceptions (such as the Crab and Vela pulsars), are likely to be binaries.

Joss, P. C.↗
DOE OSTI2020

Predicting Small Molecule Transfer Free Energies by Combining Molecular Dynamics Simulations and Deep Learning

Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on an extensive computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C–C bond scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM-Pt-Pt(111) and Pt-TM-Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. In conclusion, the approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of novel catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A curated benchmark for cofolding models on kinase conformational states

Abstract Protein kinases are critical drug targets, requiring therapeutics that can modulate their active and inactive conformational states. While cofolding models can generate global folds directly from kinase sequences and ligand SMILES strings, these models have not yet been tested on their ability to recover ligand-induced-fit conformational states of the kinase proteins. Here, we introduce KinConfBench, a curated benchmark of 2225 high-quality human kinase chains to evaluate the ability of four state-of-the-art cofolding models—Boltz-2, Chai-1, Protenix, and RoseTTAFold-All-Atom—to recover both canonical and rare conformational states. We show that geometric success metrics of a ligand pose in the active site do not correlate strongly with the correct kinase conformational state, motivating a new set of dynamical benchmarks for assessing cofolding models. While all four cofolding models achieve ~60–80% prediction accuracy for kinase conformational classification, they exhibit severe mode collapse when performing multiple inferences, show negligible structural diversity in sampling induced-fit motions, and display a prevalent “apo-drift” in which most cofolding models predominantly predict the kinase to be in its ligand-free state. Our results highlight that capturing ligand-induced protein conformational diversity, not just geometric fit, is critical for next-generation structure-based drug discovery.

Sun, Kunyang↗
DOE OSTI2024

Synthesis and magnetic properties of the Shastry-Sutherland family R 2 Be 2 SiO 7 ( R = Nd , Sm , Gd-Yb )

Compounds forming the quasi-two-dimensional Shastry-Sutherland lattice (SSL) have attracted significant experimental and theoretical attention in the field of frustrated magnetism. This is primarily due to their realization of an exactly soluble J 1 –J 2 orthogonal dimer model capable of hosting magnetic order, dimer singlet, and plaquette singlet phases in zero applied field and their complex magnetic phase diagrams with fractional magnetization plateaus and possible superfluid and supersolid phases found between the plateau states. The discovery and characterization of SSL compounds based on rare-earth magnetic ions provide a direct route to study the stability and properties of these exotic magnetic phases in systems with a variety of different magnetic anisotropies. In this paper, we discuss the synthesis and magnetic characterization of polycrystalline samples of the R 2 ⁢Be 2 ⁢SiO 7 family, where R=Nd, Sm, and Gd-Yb. All family members crystallize in the space group $P\bar{⁢4}⁢2_1⁢m$ (113) and show no signs of long-range magnetic order above 2 K, except for R=Tb which orders antiferromagnetically at 2.6 K.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Micro-Mechanically Guided High-Throughput Alloy Design Exploration Towards Metastability-Induced H Embrittlement Resistance

We develop a high-throughput approach for studying H embrittlement (HE)-resistance in alloys, which is based on combinatorial compositional screening of metastability effects by in situ scanning electron microscopy H-analyses. The project objective included: (i) technique development of high-throughput screening (HTS) for HE-resistance, (ii) discovery of new metallic materials with superior HE-resistance, (iii) Multiscale verification of HE-resistance of the new alloys and H-barrier layers, from atomic scale to an engineering scale. The investigation focused on a model system that enables exploration of different metastable states in Fe-based complex-concentrated alloys. Composition spread islands with hundreds of varying compositions are fabricated on a single substrate using a combinatorial co-sputtering technique. To screen these alloys, we developed and employed a home-built SEM-based integrated analysis system capable of characterizing H-permeability, H-trapping, H-influence on mechanical properties, and other material properties, as well as atomistic to continuum simulations to study the underlying physics.

08 HYDROGEN↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural Architecture Search is a powerful approach for automating model design, but existing methods struggle to accurately optimize for real hardware performance, often relying on proxy metrics such as bit operations. We present Surrogate Neural Architecture Codesign Package (SNAC-Pack), an integrated framework that automates the discovery and optimization of neural networks focusing on FPGA deployment.

Weitz, Jason [UC, San Diego]↗
NASA NTRS1982

Comets

Vacuum ultraviolet observations from sounding rockets and satellite observatories of the gaseous comae of several comets are reviewed. The earliest of these led to discovery of the hydrogen envelope extending for millions of km from the nucleus. Subsequent observations of H I Lyman alpha, the OH (0,0 band and the oxygen resonance triplet provided strong evidence for the water-ice model of the cometary nucleus. Several species were discovered in the coma including C, C(+), CO, S, and CS. High resolution spectroscopy and the spatial variation of the observed emissions provide means to elucidate the production and excitation mechanisms of these species. The similarity of the spectra of the half dozen comets observed to date argues for a common, homogeneous composition (with the exception of dust and CO) of the cometary ice and a minimal effect on the neutral species due to molecular collisions in the inner coma.

Wilkening, L. L.↗
NASA NTRS1987

Far-infrared activity and starburst galaxies

After the IRAS discovery of galaxies with large far-infrared to blue luminosity ratio, it has been proposed that an enhanced star formation could be the origin of the far-infrared emission through dust heating. Whether a simple photometric model is able to account for the FIR and optical properties of IRAS galaxies was investigated. The L sub IR/L sub B ratio, (B-V) color and H sub alpha equivalent width of normal spirals are well reproduced with smooth star formation histories. In the case of starburst galaxies, several theoretical diagrams allow us to estimate the burst strength and extinction. L sub IR/L sub B ratio up to 100 can be rather easily reached, whereas extreme values probably require IMF truncated at the low end.

Belfort, P.↗
NASA NTRS1996

User-Centered Indexing for Adaptive Information Access

We are focusing on information access tasks characterized by large volume of hypermedia connected technical documents, a need for rapid and effective access to familiar information, and long-term interaction with evolving information. The problem for technical users is to build and maintain a personalized task-oriented model of the information to quickly access relevant information. We propose a solution which provides user-centered adaptive information retrieval and navigation. This solution supports users in customizing information access over time. It is complementary to information discovery methods which provide access to new information, since it lets users customize future access to previously found information. It relies on a technique, called Adaptive Relevance Network, which creates and maintains a complex indexing structure to represent personal user's information access maps organized by concepts. This technique is integrated within the Adaptive HyperMan system, which helps NASA Space Shuttle flight controllers organize and access large amount of information. It allows users to select and mark any part of a document as interesting, and to index that part with user-defined concepts. Users can then do subsequent retrieval of marked portions of documents. This functionality allows users to define and access personal collections of information, which are dynamically computed. The system also supports collaborative review by letting users share group access maps. The adaptive relevance network provides long-term adaptation based both on usage and on explicit user input. The indexing structure is dynamic and evolves over time. Leading and generalization support flexible retrieval of information under similar concepts. The network is geared towards more recent information access, and automatically manages its size in order to maintain rapid access when scaling up to large hypermedia space. We present results of simulated learning experiments.

Chen, James R.↗
NASA NTRS2003

Mercury Conditions for the MESSENGER Mission Simulated in High- Solar-Radiation Vacuum Tests

The MESSENGER (Mercury Surface, Space Environment, Geochemistry, and Ranging) spacecraft, planned for launch in March 2004, will perform two flybys of Mercury before entering a year-long orbit of the planet in September 2009. The mission will provide opportunities for detailed characterization of the surface, interior, atmosphere, and magnetosphere of the closest planet to the Sun. The NASA Glenn Research Center and the MESSENGER spacecraft integrator, the Johns Hopkins University Applied Physics Laboratory, have partnered under a Space Act Agreement to characterize a variety of critical components and materials under simulated conditions expected near Mercury. Glenn's Vacuum Facility 6, which is equipped with a solar simulator, can simulate the vacuum and high solar radiation anticipated in Mercury orbit. The MESSENGER test hardware includes a variety of materials and components that are being characterized during the Tank 6 vacuum tests, where the hardware will be exposed to up to 11 suns insolation, simulating conditions expected in Mercury orbit. In 2002, ten solar vacuum tests were conducted, including beginning of life, end of life, backside exposure, and solar panel thermal shock cycling tests. Components tested include candidate solar array panels, sensors, thermal shielding materials, and communication devices. As an example, for the solar panel thermal shock cycling test, two candidate solar array panels were suspended on a lift mechanism that lowered the panels into a liquid-nitrogen-cooled box. After reaching -140 C, the panels were then lifted out of the box and exposed to the equivalent of 6 suns (8.1 kilowatts per square meters). After five cold soak/heating cycles were completed successfully, there was no apparent degradation in panel performance. An anticipated 100-hr thermal shield life test is planned for autumn, followed by solar panel flight qualification tests in winter. Glenn's ongoing support to the MESSENGER program has been instrumental in identifying design solutions and validating thermal performance models under a very aggressive development schedule. The test data have assisted Johns Hopkins engineers in selecting a flight solar array vendor and a thermal shield design. MESSENGER is one in a series of missions in NASA's Discovery Program. Infrared thermography provides data on the thermal gradients in the MESSENGER components during high solar insolation vacuum testing.

Wong, Wayne A.↗
NASA NTRS2007

Geomorphic and Aqueous Chemistry of a Portion of the Upper Rio Tinto System, Spain

Observations from the two Mars rovers, Spirit and Opportunity, combined with discoveries of extensive hydrated sulfate deposits from OMEGA and CRISM show that aqueous deposition and alteration involving acidic systems and sulfate deposition has been a key contributor to the martian geologic record. Rio Tinto, Spain, provides a process model for formation of sulfates on Mars by evaporation of acidic waters within shallow fluvial pools, particularly during dry seasons. We present results from a detailed investigation of an upper portion of the Rio Tinto, focusing on geomorphology, clastic sediment transport, and acidic aqueous processes. We also lay out lessons-learned for under-standing sulfate formation and alteration on Mars.

Osburn, M. R.↗
NASA NTRS2008

Previously Unrecognized Large Lunar Impact Basins Revealed by Topographic Data

The discovery of a large population of apparently buried impact craters on Mars, revealed as Quasi- Circular Depressions (QCDs) in Mars Orbiting Laser Altimeter (MOLA) data [1,2,3] and as Circular Thin Areas (CTAs) [4] in crustal thickness model data [5] leads to the obvious question: are there unrecognized impact features on the Moon and other bodies in the solar system? Early analysis of Clementine topography revealed several large impact basins not previously known [6,7], so the answer certainly is "Yes." How large a population of previously undetected impact basins, their size frequency distribution, and how much these added craters and basins will change ideas about the early cratering history and Late Heavy Bombardment on the Moon remains to be determined. Lunar Orbiter Laser Altimeter (LOLA) data [8] will be able to address these issues. As a prelude, we searched the state-of-the-art global topographic grid for the Moon, the Unified Lunar Control Net (ULCN) [9] for evidence of large impact features not previously recognized by photogeologic mapping, as summarized by Wilhelms [lo].

Frey, Herbert V.↗
NASA NTRSDate not supplied

Separating Magnetospheric and Heliospheric SWCX in X-ray Spectra

Solar wind charge exchange (SWCX) emission contaminates all astrophysical observations in X-rays regardless of the direction. This contamination is particularly problematic when measuring astrophysical plasma temperatures due to the similar spectral distribution of the two phenomena. Since its discovery, literature has distinguished between SWCX emission resulting from solar wind-neutral interactions within the Earth’s magnetosphere, called magnetospheric SWCX, and similar interactions occurring more generally throughout the heliosphere, called heliospheric SWCX. Previous work demonstrated accurate modeling of the heliospheric SWCX contribution for astrophysical observations performed at low ecliptic latitude with HaloSat, a CubeSat X-ray mission of medium spectral resolution. Here, we apply this model to similarly orchestrated observations to measure the magnetospheric SWCX contribution separately from the heliospheric SWCX and the astrophysical background contributions. We describe our observational strategy, the heliospheric SWCX model used, and our spectral fitting methods. In particular, we report on four observations with lines of sight through the Earth’s magnetospheric flank with total O VII line fluxes at least 3σ above that predicted by the heliospheric SWCX model and the astrophysical background, possibly indicative of magnetospheric SWCX. We then discuss the excess emission in comparison with available magnetospheric SWCX simulations requested through the Community Coordinated Modeling Center.

X-ray↗
DOE OSTI2020

Situation awareness and dynamic ensemble forecasting of abnormal behavior in cyber-physical system

A plurality of monitoring nodes may each generate a time-series of current monitoring node values representing current operation of a cyber-physical system. A feature-based forecasting framework may receive the time-series of and generate a set of current feature vectors using feature discovery techniques. The feature behavior for each monitoring node may be characterized in the form of decision boundaries that separate normal and abnormal space based on operating data of the system. A set of ensemble state-space models may be constructed to represent feature evolution in the time-domain, wherein the forecasted outputs from the set of ensemble state-space models comprise anticipated time evolution of features. The framework may then obtain an overall features forecast through dynamic ensemble averaging and compare the overall features forecast to a threshold to generate an estimate associated with at least one feature vector crossing an associated decision boundary.

Abbaszadeh, Masoud↗