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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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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks

Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H 2 , but state-of-the-art (SOTA) CeO 2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, in this study, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with materials project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ∼80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr 3 PrMn 2 O 8 and Ba 2 Fe 2 O 5 , display hydrogen yields greater than CeO 2 under specific redox conditions. In conclusion, these results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO 2 .

08 HYDROGEN↗

NASA NTRS1998

Discover Earth

Discover Earth is a NASA-sponsored project for teachers of grades 5-12, designed to: (1) enhance understanding of the Earth as an integrated system; (2) enhance the interdisciplinary approach to science instruction; and (3) provide classroom materials that focus on those goals. Discover Earth is conducted by the Institute for Global Environmental Strategies in collaboration with Dr. Eric Barron, Director, Earth System Science Center, The Pennsylvania State University; and Dr. Robert Hudson, Chair, the Department of Meteorology, University of Maryland at College Park. The enclosed materials: (1) represent only part of the Discover Earth materials; (2) were developed by classroom teachers who are participating in the Discover Earth project; (3) utilize an investigative approach and on-line data; and (4) can be effectively adjusted to classrooms with greater/without technology access. The Discover Earth classroom materials focus on the Earth system and key issues of global climate change including topics such as the greenhouse effect, clouds and Earth's radiation balance, surface hydrology and land cover, and volcanoes and climate change. All the materials developed to date are available on line at (http://www.strategies.org) You are encouraged to submit comments and recommendations about these materials to the Discover Earth project manager, contact information is listed below. You are welcome to duplicate all these materials.

Steele, Colleen↗

DOE OSTI2023

Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a reaction-diffusion type equation. However, the precise mathematical model and parameters that characterize the progression of misfolded protein across the brain remain incompletely understood. Here, we use deep learning and artificial intelligence to discover a mathematical model for the progression of Alzheimer's disease using longitudinal tau positron emission tomography from the Alzheimer's Disease Neuroimaging Initiative database. Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading. First, we demonstrate the potential of our model and parameter discovery on synthetic data. Then, we apply our method to discover the best model and parameters to explain tau imaging data from 46 individuals who are likely to develop Alzheimer's disease and 30 healthy controls. Our symbolic regression discovers different misfolding models f(c) for two groups, with a faster misfolding for the Alzheimer's group, f(c) = 0.23c 3 – 1.34c 2 + 1.11c, than for the healthy control group, f(c) = –c 3 + 0.62c 2 + 0.39c. Our results suggest that PINNs, supplemented by symbolic regression, can discover a reaction-diffusion type model to explain misfolded tau protein concentrations in Alzheimer's disease. Furthermore, we expect our study to be the starting point for a more holistic analysis to provide image-based technologies for early diagnosis, and ideally early treatment of neurodegeneration in Alzheimer's disease and possibly other misfolding-protein based neurodegenerative disorders.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

Abstract A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions. These closure models are common in many nonlinear spatiotemporal systems to account for losses due to reduced order representations, including many transport phenomena in fluids. Previous data-driven closure modeling efforts have mostly focused on supervised learning approaches using high fidelity simulation data. On the other hand, reinforcement learning (RL) is a powerful yet relatively uncharted method in spatiotemporally extended systems. In this study, we put forth a modular dynamic closure modeling and discovery framework to stabilize the Galerkin projection based reduced order models that may arise in many nonlinear spatiotemporal dynamical systems with quadratic nonlinearity. However, a key element in creating a robust RL agent is to introduce a feasible reward function, which can be constituted of any difference metrics between the RL model and high fidelity simulation data. First, we introduce a multi-modal RL to discover mode-dependant closure policies that utilize the high fidelity data in rewarding our RL agent. We then formulate a variational multiscale RL (VMRL) approach to discover closure models without requiring access to the high fidelity data in designing the reward function. Specifically, our chief innovation is to leverage variational multiscale formalism to quantify the difference between modal interactions in Galerkin systems. Our results in simulating the viscous Burgers equation indicate that the proposed VMRL method leads to robust and accurate closure parameterizations, and it may potentially be used to discover scale-aware closure models for complex dynamical systems.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1986

Will cosmic strings be discovered using the Space Telescope?

Cosmic strings are topologically stable defects in the vacuum of space which may be produced by a phase transition in the early universe. Here, it is suggested that observations of very distant galaxies are a more useful means of discovering strings than quasar observations. It is argued that if there is only one string out to redshift z - about 1 the probability that it crosses a random image obtained using the Wide Field Camera (WFC) of the Space Telescope is about 0.0001. In order to discover a cosmic string the Space Telescope WFC will be required to operate almost continuously in primary and serendipity modes, and a cosmic string, if it exists, may be discovered within the first few years of operation.

Pacynski, B.↗

NASA NTRS2005

Discovering System Health Anomalies Using Data Mining Techniques

We present a data mining framework for the analysis and discovery of anomalies in high-dimensional time series of sensor measurements that would be found in an Integrated System Health Monitoring system. We specifically treat the problem of discovering anomalous features in the time series that may be indicative of a system anomaly, or in the case of a manned system, an anomaly due to the human. Identification of these anomalies is crucial to building stable, reusable, and cost-efficient systems. The framework consists of an analysis platform and new algorithms that can scale to thousands of sensor streams to discovers temporal anomalies. We discuss the mathematical framework that underlies the system and also describe in detail how this framework is general enough to encompass both discrete and continuous sensor measurements. We also describe a new set of data mining algorithms based on kernel methods and hidden Markov models that allow for the rapid assimilation, analysis, and discovery of system anomalies. We then describe the performance of the system on a real-world problem in the aircraft domain where we analyze the cockpit data from aircraft as well as data from the aircraft propulsion, control, and guidance systems. These data are discrete and continuous sensor measurements and are dealt with seamlessly in order to discover anomalous flights. We conclude with recommendations that describe the tradeoffs in building an integrated scalable platform for robust anomaly detection in ISHM applications.

Sriastava, Ashok, N.↗

NASA NTRS2017

iPTF17cw: An Engine-Driven Supernova Candidate Discovered Independent of a Gamma-Ray Trigger

We present the discovery, classification, and radio-to-X-ray follow-up observations of iPTF17cw, a broad-lined (BL) type Ic supernova (SN) discovered by the intermediate Palomar Transient Factory (iPTF). Although it is unrelated to the gravitational wave trigger, this SN was discovered as a happy by-product of the extensive observational campaign dedicated to the follow-up of Advanced LIGO event GW 170104. The spectroscopic properties and inferred peak bolometric luminosity of iPTF17cw are most similar to the gamma-ray-burst (GRB)- associated SN, SN 1998bw, while the shape of the r-band light curve is most similar to that of the relativistic SN, SN 2009bb. Karl G. Jansky Very Large Array (VLA) observations of the iPTF17cw field reveal a radio counterpart approximately 10 times less luminous than SN 1998bw, and with a peak radio luminosity comparable to that of SN 2006aj/GRB 060218 and SN 2010bh/GRB 100316D. Our radio observations of iPTF17cw imply a relativistically expanding outflow. However, further late-time observations with the VLA in its most extended configuration are needed to confirm fading of the iPTF17cw radio counterpart at all frequencies. X-ray observations carried out with Chandra reveal the presence of an X-ray counterpart with a luminosity similar to that of SN 2010bh/GRB 100316D. Searching the Fermi catalog for possible gamma-rays reveals that GRB 161228B is spatially and temporally compatible with iPTF17cw. The similarity to SN 1998bw and SN 2009bb, the radio and X-ray detections, and the potential association with GRB 161228B all point to iPTF17cw being a new candidate member of the rare sample of optically discovered engine-driven BL-Ic SNe associated with relativistic ejecta.

Corsi, A.↗

DOE OSTI2023

Compositionally complex perovskite oxides: Discovering a new class of solid electrolytes with interface-enabled conductivity improvements

Compositionally complex ceramics (CCCs), including high-entropy ceramics, offer a vast, unexplored compositional space for materials discovery. Herein, we propose and demonstrate strategies for tailoring CCCs via a combination of non-equimolar compositional designs and control of grain boundaries (GBs) and microstructures. Using oxide solid electrolytes for all-solid-state batteries as an example, we have discovered a class of compositionally complex perovskite oxides (CCPOs) with improved lithium ionic conductivities beyond the limit of conventional doping. For example, we demonstrate that the ionic conductivity can be improved by >60% in (Li 0.375 Sr 0.4375 )(Ta 0.375 Nb 0.375 Zr 0.125 Hf 0.125 )O 3-δ compared with the (Li 0.375 Sr 0.4375 )(Ta 0.75 Zr 0.25 )O 3-δ (LSTZ) baseline. Furthermore, the ionic conductivity can be improved by another >70% via quenching, achieving >270% of the LSTZ. Notably, we demonstrate GB-enabled conductivity improvements via both promoting grain growth and altering GB structures through compositional designs and processing. In a broader perspective, this work suggests new routes for discovering and tailoring CCCs for energy storage and many other applications.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Discovering methylated DNA motifs in bacterial nanopore sequencing data with MIJAMP

Abstract Bacterial DNA methylation is involved in diverse cellular functions, including modulation of gene expression, DNA repair, and restriction–modification systems for defense against viruses and other foreign DNA. Restriction systems hinder efforts to engineer organisms to produce fuels and chemicals from waste and renewable feedstocks by degrading DNA during transformation. Methylome analysis allows identification of motifs within a bacterial chromosome that may be targeted by native restriction enzymes. Further expression of the corresponding methyltransferases in Escherichia coli allows plasmid DNA to be protected from restriction in the target organism, thereby drastically enhancing transformation efficiency. Nanopore sequencing can detect methylated bases, but software is needed to transform modified base coordinates into methylated motifs. Here, we develop MIJAMP (MIJAMP Is Just A MethylBED Parser), a software package that was developed to discover methylated motifs from the output of ONT’s Modkit or other data in the methylBED format. MIJAMP employs a human-driven refinement strategy that empirically validates all motifs against genome-wide methylation data, thus eliminating incorrect motifs. MIJAMP also reports methylation data on specific, user-defined motifs. Using MIJAMP, we determined the methylated motifs both in a control strain (wild-type E. coli) and in Synecococcus sp. strain PCC7002, laying the foundation for improved transformation in this organism. MIJAMP is available at https://code.ornl.gov/alexander-public/mijamp/. One Sentence Summary: Here we describe software written to discover DNA methylation motifs from nanopore sequencing data.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

The kinematics, metallicities, and orbits of six recently discovered Galactic star clusters with Magellan/M2FS spectroscopy

ABSTRACT We present Magellan/M2FS spectroscopy of four recently discovered Milky Way star clusters (Gran 3/Patchick 125, Gran 4, Garro 01, and LP 866) and two newly discovered open clusters (Gaia 9 and Gaia 10) at low Galactic latitudes. We measure line-of-sight velocities and stellar parameters ([Fe/H], log g, Teff, and [Mg/Fe]) from high-resolution spectroscopy centred on the Mg triplet and identify 20–80 members per star cluster. We determine the kinematics and chemical properties of each cluster and measure the systemic proper motion and orbital properties by utilizing Gaia astrometry. We find Gran 3 to be an old, metal-poor (mean metallicity of [Fe/H] = −1.83) globular cluster located in the Galactic bulge on a retrograde orbit. Gran 4 is an old, metal-poor ([Fe/H] = −1.84) globular cluster with a halo-like orbit that happens to be passing through the Galactic plane. The orbital properties of Gran 4 are consistent with the proposed LMS-1/Wukong and/or Helmi streams merger events. Garro 01 is metal-rich ([Fe/H] = −0.30) and on a near-circular orbit in the outer disc but its classification as an open cluster or globular cluster is ambiguous. Gaia 9 and Gaia 10 are among the most distant known open clusters at $R_{\mathrm{GC}}\sim 18,~21.2~\mathrm{\, kpc}$ and most metal-poor with [Fe/H] ∼−0.50, −0.34 for Gaia 9 and Gaia 10, respectively. LP 866 is a nearby, metal-rich open cluster ([Fe/H] = +0.10). The discovery and confirmation of multiple star clusters in the Galactic plane shows the power of Gaia astrometry and the star cluster census remains incomplete.

Pace, Andrew B. (ORCID:0000000260218760)↗

DOE OSTI2025

Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble

In this work we explore the capability of physics-informed neural networks (PINNs) to discover multiple solutions. Many real-world phenomena governed by nonlinear differential equations (DEs), such as fluid flow, exhibit multiple solutions under the same conditions, yet capturing this solution multiplicity remains a significant challenge. A key difficulty lies in providing appropriate initial conditions or guesses, as widely used time-marching schemes and Newton’s method are highly sensitive to these choices when solving complex computational problems. While machine learning models, particularly PINNs, have shown promise in solving DEs, their ability to capture multiple solutions remains underexplored. In this work, we propose a simple and practical approach using PINNs to learn and discover multiple solutions. We first demonstrate that PINNs, when combined with random initialization and deep ensemble method—originally developed for uncertainty quantification—can effectively uncover multiple solutions to nonlinear ordinary and partial DEs. Although training large ensembles of PINNs may appear computationally demanding, this can be done efficiently using vectorization techniques supported by modern deep learning frameworks, allowing many networks to be trained simultaneously. Our approach highlights the critical role of initialization in shaping solution diversity, addressing an often-overlooked aspect of machine learning for scientific computing. Furthermore, we propose utilizing PINN-generated solutions as initial conditions or initial guesses for conventional numerical solvers to enhance accuracy and efficiency in capturing multiple solutions. Extensive numerical experiments, including the Allen–Cahn equation and cavity flow, where our approach successfully identifies both stable and unstable solutions, validate the effectiveness of our method. These findings establish a general and efficient framework for addressing solution multiplicity in nonlinear DEs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Tools for probing new physics with newly discovered gamma-ray targets

Here, we present a computational tool, TweedleDEE, for empirically modeling diffuse gamma-ray background emission in a 1° region of the sky, using publicly available gamma-ray data off-axis from the region of interest. This background model allows a user to perform a purely data-driven search for anomalous localized sources of gamma-ray emission, including new physics. A major application of this tool would be in searching for dark matter annihilation in newly discovered astrophysical targets. For this purpose, we derive a scaling relation for determining velocity-dependent 𝐽-factors using only the stellar parameters, which can be broadly applied to obtain dark matter constraints from new targets. As an application of these tools, we use TweedleDEE and MADHATv2 to perform the first search for dark matter annihilation in the newly discovered Leo VI dwarf spheroidal galaxy, and present model constraints for a variety of choices of the annihilation channel and velocity dependence of the cross section.

dark matter↗

DOE OSTI2023

Deep reinforcement learning to discover multi-fuel injection strategies for compression ignition engines

Over the past several decades, regulation of compression ignition engine emissions has become increasingly stringent as concern about the environmental and health implications of these emissions has grown. These changing constraints have led to a series of new, alternative fuel injection strategies that aim to maintain power output while reducing in-cylinder generated emissions by operating in the low-temperature combustion (LTC) regime. These advanced injection strategies are created and retuned for individual combinations of engine geometry, fuel, and emissions constraints. Deep reinforcement learning has been shown to be an effective alternative to traditional optimization approaches for highly combinatorial control problems, such as discovering the optimal injection schedules for compression ignition engines. In this study, we deploy a previously presented deep reinforcement learning framework to iteratively optimize a series of engine geometries over a range of increasingly strict NO x emissions regulations. We then examine the resulting injection schedules. We discuss the potential for using this deep reinforcement learning framework for fuel selection screening and for discovering unique injection strategies for different engine geometries and future emissions standards.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Six More Ultra-faint Milky Way Companions Discovered in the DECam Local Volume Exploration Survey

We report the discovery of six ultra-faint Milky Way satellites identified through matched-filter searches conducted using Dark Energy Camera (DECam) data processed as part of the second data release of the DECam Local Volume Exploration (DELVE) survey. Leveraging deep Gemini/GMOS-N imaging (for four candidates) as well as follow-up DECam imaging (for two candidates), we characterize the morphologies and stellar populations of these systems. We find that these candidates all share faint absolute magnitudes (M$_{V}$ ≥ −3.2 mag) and old, metal-poor stellar populations (τ > 10 Gyr, [Fe/H] < −1.4 dex). Three of these systems are more extended (r$_{1/2}$ > 15 pc), while the other three are compact (r$_{1/2}$ < 10 pc). From these properties, we infer that the former three systems (Boötes V, Leo Minor I, and Virgo II) are consistent with ultra-faint dwarf galaxy classifications, whereas the latter three (DELVE 3, DELVE 4, and DELVE 5) are likely ultra-faint star clusters. Using data from the Gaia satellite, we confidently measure the proper motion of Boötes V, Leo Minor I, and DELVE 4, and tentatively detect a proper-motion signal from DELVE 3 and DELVE 5; no signal is detected for Virgo II. We use these measurements to explore possible associations between the newly discovered systems and the Sagittarius dwarf spheroidal, the Magellanic Clouds, and the Vast Polar Structure, finding several plausible associations. Our results offer a preview of the numerous ultra-faint stellar systems that will soon be discovered by the Vera C. Rubin Observatory and highlight the challenges of classifying the faintest stellar systems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1991

Tentative identification of a newly discovered class of material on Io

A newly discovered absorption feature in Io's spectrum at 2.1253 microns promises to reveal significant insights in the nature of the interaction among Io's surface, atmosphere, and volcanoes. The most likely candidate for this feature appears to be clusters of CO2 molecules. Observations are underway on a current series of occultations and eclipses of Io by other Galilean satellites in order to locate the source of this newly discovered material on Io, which should provide further clues to its origin.

Trafton, L.↗

NASA NTRS1996

Discover Earth

Discover Earth is a NASA-funded project for teachers of grades 5-12 who want to expand their knowledge of the Earth system, and prepare to become master teachers who promote Earth system science in their own schools, counties, and throughout their state. Participants from the following states are invited to apply: Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Washington, DC. Teachers selected for the project participate in a two-week summer workshop conducted at the University of Maryland, College Park; develop classroom-ready materials during the workshop for broad dissemination; conduct a minimum of two peer training activities during the coming school year; and participate in other enrichment/education opportunities as available and desired. Discover Earth is a team effort that utilizes expertise from a range of contributors, and balances science content with hands-on classroom applications.

Steele, Colleen↗

NASA NTRS1997

Discover Earth

Source record↗