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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

High entropy oxides prediction and discovery by the Mixed Enthalpy-Entropy Descriptor

The vast, high-dimensional composition space of high-entropy oxides (HEOs) offers exceptional opportunities for functional materials discovery, yet it also poses a fundamental challenge: the rational and efficient prediction of stable, synthesizable compositions and the corresponding structure–property relationships. Despite growing interest, the field still lacks broadly applicable, physically grounded descriptors capable of navigating various large chemical spaces. Here, we introduce a Mixed Enthalpy–Entropy Descriptor (MEED) that enables rapid, first-principles–based prediction of HEOs synthesizability across diverse chemistries. Using MEED, we perform high-throughput screening of two distinct HEO families: rocksalt oxides and perovskite oxides. The predicted top candidates in each family were experimentally validated. MEED reveals unifying thermodynamic and structural principles governing stability across both chemical compositions and polymorphs, providing mechanistic insight into the formation of high-entropy phases. This work significantly broadens the accessible chemical design space for HEOs and establishes a data-efficient framework for accelerating the discovery of next-generation functional materials.

Yu, Liping [University of Central Florida]↗
DOE OSTI2026

Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an ecosystem of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic “AI scientist”. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Highlights of Discoveries for δ Scuti Variable Stars from the Kepler Era

The NASA Kepler and follow-on K2 mission (2009–2018) left a legacy of data and discoveries, finding thousands of exoplanets, and also obtaining high-precision long time-series data for hundreds of thousands of stars, including many types of pulsating variables. Here we highlight a few of the ongoing discoveries from Kepler data on δ Scuti pulsating variables, which are core hydrogen-burning stars of about twice the mass of the Sun. We discuss many unsolved problems surrounding the properties of the variability in these stars, and the progress enabled by Kepler data in using pulsations to infer their interior structure, a field of research known as asteroseismology.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data

Multi-omics data offers rich insights into complex traits across organisms, yet integrating and analyzing these datasets for phenotype prediction and marker discovery remains challenging. Researchers need accessible tools that combine deep learning, hyperparameter optimization, visualization, and downstream analysis in a unified web platform. To address this, we developed G2PDeep-v2, a web-based platform powered by deep learning for phenotype prediction and marker discovery from multi-omics data across a wide range of organisms, including humans and plants. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Discovery, Timing, and Multiwavelength Observations of the Black Widow Millisecond Pulsar PSR J1555–2908

Abstract We report the discovery of PSR J1555−2908, a 1.79 ms radio and gamma-ray pulsar in a 5.6 hr binary system with a minimum companion mass of 0.052 M ⊙ . This fast and energetic ( E ̇ = 3 × 10 35 erg s −1 ) millisecond pulsar was first detected as a gamma-ray point source in Fermi Large Area Telescope (LAT) sky survey observations. Guided by a steep-spectrum radio point source in the Fermi error region, we performed a search at 820 MHz with the Green Bank Telescope that first discovered the pulsations. The initial radio pulse timing observations provided enough information to seed a search for gamma-ray pulsations in the LAT data, from which we derive a timing solution valid for the full Fermi mission. In addition to the discovery and timing of radio and gamma-ray pulsations, we searched for X-ray pulsations using NICER but no significant pulsations were detected. We also obtained time-series r -band photometry that indicates strong heating of the companion star by the pulsar wind. Material blown off the heated companion eclipses the 820 MHz radio pulse during inferior conjunction of the companion for ≈10% of the orbit, which is twice the angle subtended by its Roche lobe in an edge-on system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Pegasus IV: Discovery and Spectroscopic Confirmation of an Ultra-faint Dwarf Galaxy in the Constellation Pegasus

We report the discovery of Pegasus IV, an ultra-faint dwarf galaxy found in archival data from the Dark Energy Camera processed by the DECam Local Volume Exploration Survey. Pegasus IV is a compact, ultra-faint stellar system ( pc; M $_{V}$ = −4.25 ± 0.2 mag) located at a heliocentric distance of . Based on spectra of seven nonvariable member stars observed with Magellan/IMACS, we confidently resolve Pegasus IV’s velocity dispersion, measuring km s$^{−1}$ (after excluding three velocity outliers); this implies a mass-to-light ratio of for the system. From the five stars with the highest signal-to-noise spectra, we also measure a systemic metallicity of [Fe/H] = dex, making Pegasus IV one of the most metal-poor ultra-faint dwarfs. We tentatively resolve a nonzero metallicity dispersion for the system. These measurements provide strong evidence that Pegasus IV is a dark-matter-dominated dwarf galaxy, rather than a star cluster. We measure Pegasus IV’s proper motion using data from Gaia Early Data Release 3, finding (μ $_{α*}$, μ $_{δ}$) = (0.33 ± 0.07, −0.21 ± 0.08) mas yr$^{−1}$. When combined with our measured systemic velocity, this proper motion suggests that Pegasus IV is on an elliptical, retrograde orbit, and is currently near its orbital apocenter. Lastly, we identify three potential RR Lyrae variable stars within Pegasus IV, including one candidate member located more than 10 half-light radii away from the system’s centroid. The discovery of yet another ultra-faint dwarf galaxy strongly suggests that the census of Milky Way satellites is still incomplete, even within 100 kpc.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Discovery of a Gamma-Ray Black Widow Pulsar by GPU-accelerated Einstein@Home

We report the discovery of 1.97 ms period gamma-ray pulsations from the 75 minute orbital-period binary pulsar now named PSR J1653-0158. The associated Fermi Large Area Telescope gamma-ray source 4FGL J1653.6-0158 has long been expected to harbor a binary millisecond pulsar. Despite the pulsar-like gamma-ray spectrum and candidate optical/X-ray associations—whose periodic brightness modulations suggested an orbit—no radio pulsations had been found in many searches. The pulsar was discovered by directly searching the gamma-ray data using the GPU-accelerated Einstein@Home distributed volunteer computing system. The multidimensional parameter space was bounded by positional and orbital constraints obtained from the optical counterpart. More sensitive analyses of archival and new radio data using knowledge of the pulsar timing solution yield very stringent upper limits on radio emission. Any radio emission is thus either exceptionally weak, or eclipsed for a large fraction of the time. The pulsar has one of the three lowest inferred surface magnetic-field strengths of any known pulsar with B surf ≈ 4 × 10 7 G. The resulting mass function, combined with models of the companion star's optical light curve and spectra, suggests a pulsar mass gsim 2 M ⊙ . The companion is lightweight with mass ~0.01 M ⊙ , and the orbital period is the shortest known for any rotation-powered binary pulsar. This discovery demonstrates the Fermi Large Area Telescope's potential to discover extreme pulsars that would otherwise remain undetected.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

FAST Discovery of a Fast Neutral Hydrogen Outflow

Abstract In this letter, we report the discovery of a fast neutral hydrogen outflow in SDSS J145239.38+062738.0, a merging radio galaxy containing an optical type I active galactic nucleus (AGN). This discovery was made through observations conducted by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) using redshifted 21 cm absorption. The outflow exhibits a blueshifted velocity likely up to ∼−1000 km s −1 with respect to the systemic velocity of the host galaxy with an absorption strength of ∼−0.6 mJy beam −1 corresponding to an optical depth of 0.002 atv= −500 km s −1 . The mass outflow rate ranges between 2.8 × 10 −2 and 3.6M ⊙ yr −1 , implying an energy outflow rate ranging between 4.2 × 10 39 and 9.7 × 10 40 erg s −1 , assuming 100 K s< 1000 K. Plausible drivers of the outflow include the starbursts, AGN radiation, and radio jet, the last of which is considered the most likely culprit according to the kinematics. By analyzing the properties of the outflow, AGN, and jet, we find that if the Hioutflow is driven by the AGN radiation, the AGN radiation does not seem powerful enough to provide negative feedback, whereas the radio jet shows the potential to provide negative feedback. Our observations contribute another example of a fast outflow detected in neutral hydrogen and demonstrate the capability of FAST in detecting such outflows.

Astronomy & Astrophysics↗
DOE OSTI2023

Human Factors in Discovery Phase of TRLs and HRLs

With rapid growth in technology, there has been a corresponding growth in research focused on the ways that human-machine interactions can be improved. As part of that work, researchers have explored how human expertise can inform technology design and evaluation. For example, interaction with subject matter experts (SMEs) or end users can help to design and enhance a machine. The human factors of technology release can be divided into five steps: discovery, planning, development, evaluation, and deployment. This framework is a higher-level abstraction of the Human Readiness Levels for technology use and adoption (See, et al., 2018). In this exposition, we discuss how human factors methodologies, principles, and practices can be realized in the first phase, Discovery, of the technology development process.

Jefferson, Brett A.↗
DOE OSTI2026

Discovery and Spectroscopic Characterization of a Distant, Compact Milky Way Satellite in Gemini

We present the discovery of a compact Milky Way satellite in the constellation of Gemini. This system was discovered by cross-matching detections from two independent search algorithms applied to Blanco/DECam data from the third data release of the DECam Local Volume Exploration survey (DELVE DR3), and confirmed with deeper imaging from Gemini/GMOS-N. Based on these data, we determine that the system is an ultra-faint ($M_V = -2.1^{+0.4}_{-0.6}$), compact ($r_{1/2} = 8.6^{+1.4}_{-1.2}$ pc) system located at a heliocentric distance of $120^{+7}_{-6}$ kpc. These physical properties place the system in the regime of ambiguous, ultra-faint compact Milky Way halo satellites that cannot be confidently classified as dwarf galaxies or star clusters from morphology alone; we therefore name the system DELVE 8/Gemini I. From medium-resolution Keck/DEIMOS spectroscopy, we securely identify four members including two blue horizontal branch stars, confirming the system as a bound satellite moving at a mean radial velocity of $v_{\rm hel} = -82.7^{+3.7}_{-3.9} {\rm km\,s}^{-1}$. We also use these spectra to place an upper limit of $\rm [Fe/H] \lesssim -2.5$ on the metallicity of DELVE 8/Gemini I's brightest star, supporting the classification of the system as either an ancient star cluster or ultra-faint dwarf galaxy. The discovery of faint, distant systems similar to DELVE 8/Gemini I is expected to become more common with upcoming surveys.

Overdeck, K. [Chicago U., Astron. Astrophys. Ctr.;↗
DOE OSTI2025

Constrained or unconstrained? Neural-network-based equation discovery from data

Throughout many fields, practitioners often rely on differential equations to model systems. Yet, for many applications, the theoretical derivation of such equations and/or the accurate resolution of their solutions may be intractable. Instead, recently developed methods, including those based on parameter estimation, operator subset selection, and neural networks, allow for the data-driven discovery of both ordinary and partial differential equations (PDEs), on a spectrum of interpretability. The success of these strategies is often contingent upon the correct identification of representative equations from noisy observations of state variables and, as importantly and intertwined with that, the mathematical strategies utilized to enforce those equations. Specifically, the latter has been commonly addressed via unconstrained optimization strategies. Representing the PDE as a neural network, we propose to discover the PDE (or the associated operator) by solving a constrained optimization problem and using an intermediate state representation similar to a physics-informed neural network (PINN). The objective function of this constrained optimization problem promotes matching the data, while the constraints require that the discovered PDE is satisfied at a number of spatial collocation points. We present a penalty method and a widely used trust-region barrier method to solve this constrained optimization problem, and we compare these methods on numerical examples. Our results on several example problems demonstrate that the latter constrained method outperforms the penalty method, particularly for higher noise levels or fewer collocation points. This work motivates further exploration into using sophisticated constrained optimization methods in scientific machine learning, as opposed to their commonly used, penalty-method or unconstrained counterparts. For both of these methods, we solve these discovered neural network PDEs with classical methods, such as finite difference methods, as opposed to PINNs-type methods relying on automatic differentiation. Here, we briefly highlight how simultaneously fitting the data while discovering the PDE improves the robustness to noise and other small, yet crucial, implementation details.

Data-driven discovery↗
DOE OSTI2021

Early Drug Discovery and Development of Novel Cancer Therapeutics Targeting DNA Polymerase Eta (POLH)

Polymerase eta (or Pol η or POLH) is a specialized DNA polymerase that is able to bypass certain blocking lesions, such as those generated by ultraviolet radiation (UVR) or cisplatin, and is deployed to replication foci for translesion synthesis as part of the DNA damage response (DDR). Inherited defects in the gene encoding POLH (a.k.a., XPV) are associated with the rare, sun-sensitive, cancer-prone disorder, xeroderma pigmentosum, owing to the enzyme’s ability to accurately bypass UVR-induced thymine dimers. In standard-of-care cancer therapies involving platinum-based clinical agents, e.g., cisplatin or oxaliplatin, POLH can bypass platinum-DNA adducts, negating benefits of the treatment and enabling drug resistance. POLH inhibition can sensitize cells to platinum-based chemotherapies, and the polymerase has also been implicated in resistance to nucleoside analogs, such as gemcitabine. POLH overexpression has been linked to the development of chemoresistance in several cancers, including lung, ovarian, and bladder. Co-inhibition of POLH and the ATR serine/threonine kinase, another DDR protein, causes synthetic lethality in a range of cancers, reinforcing that POLH is an emerging target for the development of novel oncology therapeutics. Using a fragment-based drug discovery approach in combination with an optimized crystallization screen, we have solved the first X-ray crystal structures of small novel drug-like compounds, i.e., fragments, bound to POLH, as starting points for the design of POLH inhibitors. The intrinsic molecular resolution afforded by the method can be quickly exploited in fragment growth and elaboration as well as analog scoping and scaffold hopping using medicinal and computational chemistry to advance hits to lead. An initial small round of medicinal chemistry has resulted in inhibitors with a range of functional activity in an in vitro biochemical assay, leading to the rapid identification of an inhibitor to advance to subsequent rounds of chemistry to generate a lead compound. Importantly, our chemical matter is different from the traditional nucleoside analog-based approaches for targeting DNA polymerases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Discovery and characterization of a selective IKZF2 glue degrader for cancer immunotherapy

Malignant tumors can evade destruction by the immune system by attracting immune-suppressive regulatory T cells (Treg) cells. The IKZF2 (Helios) transcription factor plays a crucial role in maintaining function and stability of Treg cells, and IKZF2 deficiency reduces tumor growth in mice. Here we report the discovery of NVP-DKY709, a selective molecular glue degrader of IKZF2 that spares IKZF1/3. We describe the recruitment-guided medicinal chemistry campaign leading to NVP-DKY709 that redirected the degradation selectivity of cereblon (CRBN) binders from IKZF1 toward IKZF2. Selectivity of NVP-DKY709 for IKZF2 was rationalized by analyzing the DDB1:CRBN:NVP-DKY709:IKZF2(ZF2 or ZF2-3) ternary complex X-ray structures. Exposure to NVP-DKY709 reduced the suppressive activity of human Treg cells and rescued cytokine production in exhausted T-effector cells. In vivo, treatment with NVP-DKY709 delayed tumor growth in mice with a humanized immune system and enhanced immunization responses in cynomolgus monkeys. NVP-DKY709 is being investigated in the clinic as an immune-enhancing agent for cancer immunotherapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Aggregate attack surface management for network discovery of operational technology

Interconnectivity has become a substratum of technology as the benefits of data-driven functionality are being realized in nearly all industries. Increased connectivity of Operational Technology (OT) exacerbates cyber risks because Industrial Control Systems (ICS) are becoming exposed to the Internet. These exposures are often done inadvertently through misconfigurations as additional network devices come online. Attack surface management (ASM) platforms can be used to identify vulnerabilities by performing external network discovery over the Internet using web spiders. These web spiders enable big data analytics of Internet of Things (IoT) devices as identifiable information of Internet-exposed equipment are archived in searchable databases that are made publicly available. There are a multitude of ASM service providers on the market. Here, this study was conducted to evaluate several commonly known tools to determine the aggregate attack surface of control systems. Queries were crafted by targeting commonly known manufacturers and communication protocols found in OT networks. Identified devices were that categorized based on technology types. Each query was replicated between several tools to target identical ICS equipment. Findings in this paper suggested a significant variance in the exposures discovered by each tool, but unique contributions were identified for each tool when a merged attack surface was derived. Therefore, all tools should be used in aggregate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A network-enabled pipeline for gene discovery and validation in non-model plant species

Identifying key regulators of important genes in non-model crop species is challenging due to limited multi-omics resources. To address this, we introduce the network-enabled gene discovery pipeline NEEDLE, a user-friendly tool that systematically generates coexpression gene network modules, measures gene connectivity, and establishes network hierarchy to pinpoint key transcriptional regulators from dynamic transcriptome datasets. After validating its accuracy with two independent datasets, we applied NEEDLE to identify transcription factors (TFs) regulating the expression of cellulose synthase-like F6 ( CSLF6 ), a crucial cell wall biosynthetic gene, in Brachypodium and sorghum. Our analyses uncover regulators of CSLF6 and also shed light on the evolutionary conservation or divergence of gene regulatory elements among grass species. These results highlight NEEDLE’s capability to provide biologically relevant TF predictions and demonstrate its value for non-model plant species with dynamic transcriptome datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Omics-guided metabolic pathway discovery in plants: Resources, approaches, and opportunities

Plants produce a vast array of metabolites, the biosynthetic routes of which remain largely undetermined. Genome-scale enzyme and pathway annotations and omics technologies have revolutionized research to decrypt plant metabolism and produced a growing list of functionally characterized metabolic genes and pathways. However, what is known is still a tiny fraction of the metabolic capacity harbored by plants. Here, in this work, we review plant enzyme and pathway annotation resources and cutting-edge omics approaches to guide discovery and characterization of plant metabolic pathways. We also discuss strategies for improving enzyme function prediction by integrating protein 3D structure information and single cell omics. This review aims to serve as a primer for plant biologists to leverage omics datasets to facilitate understanding and engineering plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Language models for materials discovery and sustainability: Progress, challenges, and opportunities

Significant advancements have been made in one of the most critical branches of artificial intelligence: natural language processing (NLP). These advancements are exemplified by the remarkable success of OpenAI’s GPT-3.5/4 and the recent release of GPT-4.5, which have sparked a global surge of interest akin to an NLP gold rush. Here, in this article, we offer our perspective on the development and application of NLP and large language models (LLMs) in materials science. We begin by presenting an overview of recent advancements in NLP within the broader scientific landscape, with a particular focus on their relevance to materials science. Next, we examine how NLP can facilitate the understanding and design of novel materials and its potential integration with other methodologies. To highlight key challenges and opportunities, we delve into three specific topics: (i) the limitations of LLMs and their implications for materials science applications, (ii) the creation of a fully automated materials discovery pipeline, and (iii) the potential of GPT-like tools to synthesize existing knowledge and aid in the design of sustainable materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

42 ENGINEERING↗