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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2025

E-Area Low-Level Waste Facility Inadvertent Human Intruder Limits and Doses in Support of the PA2022

This report documents the inadvertent human intruder (IHI) analysis for the E-Area Low-Level Waste Facility (ELLWF) at the Savannah River Site (SRS), near Aiken, South Carolina. This analysis supports the revised ELLWF Performance Assessment (PA), complying with the Department of Energy standard for operation of low-level waste disposal facilities (USDOE, 2017). The ELLWF is an operating waste disposal facility and is scheduled to continue accepting waste to 2065. One task of the revised PA is to establish waste inventory limits for the various disposal units at ELLWF. This is done by modeling future contaminant release and transport through applicable pathways to human receptors, comparing predicted doses per disposed curie with applicable performance measures, to obtain inventory limits which will assure that doses to receptors do not exceed performance measures. This report documents results of modeling future doses to one class of receptor, the inadvertent human intruder. It is assumed that after site closure, public knowledge of the site is lost, and IHIs will engage in activities on the ELLWF that will disrupt the closure cap, causing dose to the IHI. Following USDOE (2017), six different stylized exposure scenarios are considered, simulating activities by an IHI which could result in a radiological dose. The six scenarios are: • Acute – Basement Construction: IHI constructs a basement and encounters waste during excavation which is inadvertently mixed with clean soil and diluted. • Acute – Well Drilling: IHI drills a water well through waste and is exposed to drill cuttings mixed with clean soil that are brought to the surface. • Acute – Discovery: IHI begins constructing a basement but stops when encountering the riprap in the final closure cap and is exposed to photon radiation from unexcavated material residing in the undisturbed waste zone. • Chronic – Agriculture: Resident IHI is exposed to waste that was excavated for basement construction and mixed with native soil in the intruder’s vegetable garden. • Chronic – Post-Drilling: Resident IHI is exposed to waste from drill cuttings mixed with native soil and scattered in the garden area. • Chronic – Residential: Resident IHI is exposed to external radiation while in home located above waste with shielding provided by the concrete basement floor and any soil or engineered material remaining between the basement and waste. Dose calculations are performed using the SRNL Dose Toolkit (Aleman, 2023), following the approach of Smith et al (2019). Calculations are performed separately for 27 of the 33 disposal units (DUs) at ELLWF and are radionuclide specific. The results of the IHI analysis include: • Dose Factors: mrem per disposed curie (acute) and mrem/yr per disposed curie (chronic) for each parent radionuclide, for each DU. • Inventory Limits: in curies, for each parent radionuclide, for each DU. • Estimated Dose to IHI: mrem (acute) and mrem/yr (chronic), for each DU, given its projected closure inventory without inventory biases applied. Most DU-specific IHI inventory limits are in the range of 10 3 to 10 7 curies per nuclide. The lowest inventory limits are associated with gamma-emitters such as Sn-126, Ra-226, Th-232, and Cm-248. Radionuclides with short half-lives such as Pu-241, and nuclides which are pure beta emitters or which decay by electron capture, such as Ni-59 and Ni-63, have the highest limits. For the 27 evaluated DUs, predicted IHI doses are shown in Table ES-1. The maximum acute dose is 1.18 mrem, at ST23, much less than the DOE performance measure of 500 mrem (USDOE, 2017). The highest chronic dose is 37.2 mrem/yr at ST02, below the DOE performance measure of 100 mrem/yr. Also shown are estimated inventory sums of fractions (SOFs) at closure in 2065, for groundwater (GW) and IHI pathways. For each DU, the inventory is constrained by the GW pathway. For most DUs, the IHI SOFs are approximately 1000 times lower than the GW SOF values, and the IHI pathway does not drive risk for any disposal unit.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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 OSTI2024

Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Enrichable consortia of microbial symbionts degrade macroalgal polysaccharides in Kyphosus fish

ABSTRACT Coastal herbivorous fishes consume macroalgae, which is then degraded by microbes along their digestive tract. However, there is scarce genomic information about the microbiota that perform this degradation. This study explores the potential of Kyphosus gastrointestinal microbial symbionts to collaboratively degrade and ferment polysaccharides from red, green, and brown macroalgae through in silico study of carbohydrate-active enzyme and sulfatase sequences. Recovery of metagenome-assembled genomes (MAGs) from previously described Kyphosus gut metagenomes and newly sequenced bioreactor enrichments reveals differences in enzymatic capabilities between the major microbial taxa in Kyphosus guts. The most versatile of the recovered MAGs were from the Bacteroidota phylum, whose MAGs house enzyme collections able to decompose a variety of algal polysaccharides. Unique enzymes and predicted degradative capacities of genomes from the Bacillota (genus Vallitalea ) and Verrucomicrobiota (order Kiritimatiellales ) highlight the importance of metabolic contributions from multiple phyla to broaden polysaccharide degradation capabilities. Few genomes contain the required enzymes to fully degrade any complex sulfated algal polysaccharide alone. The distribution of suitable enzymes between MAGs originating from different taxa, along with the widespread detection of signal peptides in candidate enzymes, is consistent with cooperative extracellular degradation of these carbohydrates. This study leverages genomic evidence to reveal an untapped diversity at the enzyme and strain level among Kyphosus symbionts and their contributions to macroalgae decomposition. Bioreactor enrichments provide a genomic foundation for degradative and fermentative processes central to translating the knowledge gained from this system to the aquaculture and bioenergy sectors. IMPORTANCE Seaweed has long been considered a promising source of sustainable biomass for bioenergy and aquaculture feed, but scalable industrial methods for decomposing terrestrial compounds can struggle to break down seaweed polysaccharides efficiently due to their unique sulfated structures. Fish of the genus Kyphosus feed on seaweed by leveraging gastrointestinal bacteria to degrade algal polysaccharides into simple sugars. This study reconstructs metagenome-assembled genomes for these gastrointestinal bacteria to enhance our understanding of herbivorous fish digestion and fermentation of algal sugars. Investigations at the gene level identify Kyphosus guts as an untapped source of seaweed-degrading enzymes ripe for further characterization. These discoveries set the stage for future work incorporating marine enzymes and microbial communities in the industrial degradation of algal polysaccharides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Automation of Vulnerability and Patch Management: Information Extraction, Association, and Optimization

Vulnerability and patch management is an integral part of a robust cybersecurity program, yet it grows increasingly complex due to the sheer amount of data that must be analyzed. Particularly in Operational Technology (OT) environments, analysis must be done manually because of the lack of automated solutions. Additionally, there are many steps in this process, from the initial discovery of the vulnerability to the implementation of its remediation, and each step in the process requires different data in order to be performed effectively. In this work, we provide approaches and strategies to assist operators in industrial or OT environments throughout the vulnerability management cycle. Security advisories provide key information about mitigation strategies, or actions that can be taken when a patch is unavailable or cannot be installed. Details of these strategies are not shared in public vulnerability databases and must be found manually. We approach this problem by designing a solution to automatically identify that information within vendor security advisories and retrieve it for operator use. We start with an approach that requires domain-specific knowledge of certain frequently-seen reference websites. Next, an approach that can work on an arbitrary website but relies on certain keywords. Finally, an approach that uses Natural Language Processing (NLP) methods and does not require specific knowledge or keywords. Each of these approaches is more general than its predecessor; we demonstrate high accuracy for all approaches Advisories also often contain details of affected products in non-standard or natural language formats. While this information can be easily understood when read by an operator, the non-standard format acts as a barrier to effective automation. We provide an approach for the first step in this process: identifying vendors in security advisories and mapping them to a standard framework for representing digital assets and software products. We evaluate five established string similarity algorithms, plus one of our own design that combines string similarity and information theory, on the task of mapping vendors to their corresponding entries in the Common Platform Enumeration (CPE) repository. Our results show that our proposed metric outperforms all others. Due to the constraints on time, finances, and personnel for organizations, Large Language Models (LLMs) may seem like attractive opportunities for security operators to speed up information gathering; however, it is still not clear whether LLMs can handle vulnerability management tasks well. To answer this question, we perform an empirical study of LLMs’ ability to provide consistent, accurate information about vulnerabilities in order to guide organizations in their adoption of LLMs. We observe poor performance for all models tested, suggesting that these models are not well-suited to the consistent retrieval of accurate vulnerability information. Finally, once vulnerabilities have been identified and any additional information has been obtained, operators must decide which remediation actions to implement based on their available resources. This already-complex problem becomes even more so when we consider that a vulnerability may have multiple avenues for remediation. We formulate this scenario as two knapsack problems and provide solutions, which we then compare against several existing strategies for vulnerability prioritization seen in real operational environments.

McClanahan, Kylie↗
DOE OSTI2026

Optimization techniques in self-similar compressible flow

We investigate the one-dimensional (1D) inviscid compressible flow equations for an ideal gas through the lens of optimization techniques. It is the case that, to our knowledge, optimization analysis applied to the so-called “linear velocity” solutions of the Euler compressible flow equations has not been previously conducted. Through both gradient-based and variational techniques, new variants of well-studied flow scenarios, i.e., self-similar, 1D, linear velocity solution class to idealized inviscid compressible flow equations, are determined, as encoded in both the kinematic and thermodynamic properties of this self-similar solution class. With the kinematics of the said solutions being driven by a self-similar “scale radius” and the thermodynamics being driven separately through the appearance of an arbitrary function, a myriad of new solution classes is possible. Acting as a guide to more realistic physical circumstances as well as discovery, it is the hope that the presented cases serve as the framework for future investigations into the intersection of self-similarity and optimization techniques. Fields of study that may find this work to be of interest include aerodynamic design, flow control, inertial confinement fusion, physics-informed neural networks, and other related areas of interest.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2025

RAG for FLAG: AI Assistance for a Physics Code

Artificial intelligence (AI) has quickly become an important tool in scientific research, where significant efforts are underway to develop tools that will expedite the research process. One area of particular impact is scientific software, which can be particularly complex, and therefore time consuming to learn and use effectively. AI assistants are increasingly helping to streamline the process by performing tasks such as interactively answering user questions or suggesting solutions. Los Alamos National Laboratory (LANL) develops several advanced scientific codes, such as FLAG, which can be used to run multiphysics simulations. With this study, our goal was to develop an AI assistant for FLAG that could help make the process of understanding the software and running physics simulations more efficient. To develop an AI assistant for FLAG, we used a method called retrieval-augmented generation (RAG), which is a technique that uses information from relevant data sources to enhance the accuracy of large language models (LLMs). We used the FLAG user manual and other FLAG documentation as the knowledge base for the RAG system. When a user provides a query, RAG retrieves relevant sections from the knowledge base in response, then uses those excerpts to generate grounded and contextually rich answers. We found that our AI assistant was able to provide context aware answers and source references to user queries. To evaluate performance, we developed a set of 40 benchmark questions and compared the accuracy of the responses to those of two standard LLMs without retrieval. Our AI assistant significantly outperformed the standard LLMs at answering FLAG-related questions, with an 82.5% accuracy rate, compared to 47.5% for both of the standard LLMs. This has the potential to make the process of learning and using FLAG much easier, especially for new users. Ultimately, it supports LANL’s broader mission by empowering scientists and engineers to focus more on discovery and analysis rather than on navigating complex software systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The United States Department of Energy and National Institutes of Health Collaboration: Medical Care Advances by Discovery in Radiation Detection

A National Institutes of Health (NIH) and U.S. Department of Energy (DOE) Office of Science virtual workshop on shared general topics was held in July of 2021 and reported on in this publication in January of 2023. Following the inaugural 2021 joint meeting representatives from the DOE Office of Science and NIH met to discuss organizing a second joint workshop that would concentrate on radiation detection to bring together teams from both agencies and their grantee populations to stimulate collaboration and efficiency. To meet this scientific mission within the NIH and DOE radiation detection space, the organizers assembled workshop sessions covering the state–of–the–art in cameras, detectors, and sensors for radiation external and internal (diagnostic and therapeutic) to human, data acquisition and electronics, image reconstruction and processing, and the application of artificial intelligence. NIH and DOE are committed to continuing the process of convening a joint workshop every 12–24 months. This Special Report recaps the findings of this second workshop. Beyond showing only the innovations and areas of success, important gaps in our knowledge were defined and presented. Finally, we summarize by defining four areas of greatest opportunity and need that emerged from the unique, dynamic dialogue the in–person workshop provided the attendees.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Data Cards for Standardized Metadata Across DOE-Aligned Data Initiatives: Toward Transparent, Interoperable, and Governed Dataset Documentation

As data-intensive research, advanced computing, and artificial intelligence become increasingly central to scientific and operational workflows, the need for consistent, transparent, and machine-actionable documentation has grown correspondingly. Multiple DOE-aligned communities—including Office of Science, Genesis Mission, American Science Cloud (AmSC), National Nuclear Security Administration (NNSA) stewardship and governance, and related cross-laboratory collaborations—have independently developed metadata practices to support discovery, access, reuse, repository deposit, and compliance.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Exploring the Nature of f-Element Soft Donor Interactions Using Electronically Tunable Azolate Ionic Liquids

This project was undertaken to advance the understanding of how f-elements interact with moderately soft donors, a heavily investigated yet open question which is of prime importance in spent nuclear fuel processing and fundamental inorganic chemistry. During the course of the project, based on exciting results, a stretch goal was developed to study the hydrolysis products of transuranic actinide metals, a somewhat understudied field even with its significance in nuclear fuel cycle and impacts in environmental chemistry. The stretch goal was to take our serendipitous discovery of an easy route to isolation of crystalline multinuclear ƒ-element hydrolysis products, and apply it to gaining a mechanistic understanding of Pu(III/IV) colloid formation. The simplicity of our techniques should lend themselves to the remote handling required for study of many transuranic elements. We developed several methodologies using azolium azolate chemistry to overcome ƒ-element hydrolysis problems that prohibit the isolation of ƒ-element soft donor complexes and to build a crystallographic library of ƒ-element N-donor complexes as a means to understand the fundamental differences between actinide and lanthanide interactions with moderately soft donor ligands. Our next major endeavor will be to transfer this chemistry from 4ƒ elements to transuranic elements, particularly in the study of hydrolysis of Pu(III/IV). While our work is fundamental in nature, applications of the knowledge we are generating should be felt in such diverse fields as catalysis, separations in general, nuclear waste remediation specifically, and many other applications in f-element magnetic and luminescent properties. The potential ramifications of the consistent and robust formation of hydrolysis controlled hexanuclear lanthanide structures are enormous, with future uses being catalyst formation, higher-nuclearity structure synthesis using our hexanuclear motif as a fundamental building block, Pu waste remediation, separations, and many other potential applications resulting from characteristic magnetic and luminescent properties of lanthanide polynuclear structures. Three synthetic methodologies (direct mixing with variable stoichiometries, use of volatile solvent, metathesis) were developed starting with 7 acidic and 6 basic azoles to obtain ionic liquids suitable for f-element coordination. Proton transfer by acidic/basic azole combination led to suitable low melting salts and two cocrystals. Acid/base reaction of azoles with soft-donor permanent cations of ([X 4444 ][OH] (where [X 4444 ] + = tetrabutylammonium [N 4444 ] + or tetrabutylphosphonium [P 4444 ] + ) with weakly acidic azoles including imidazole, 1,2,3-triazole, 1,2,4-triazole, 5-aminotetrazole, 4,5-dicyanoimidazole, and 2-amino-4,5-dicyanoimidazole) revealed several suitable low-melting salts. Metathesis reactions of Na(azolate) were conducted by first using weakly acidic azoles including 4,5-dicyanoimidazole, 2-amino-4,5-dicyanoimidazole, 5-aminotetrazole, and 1,2,4-triazole to form sodium or lithium salts using group(I) hydroxides in methanolic solutions. The best results were obtained by reacting the basic and acidic azoles directly in 1:1 or 3:1 ratios at elevated temperatures. Twenty-two azole mixtures were identified which are either low melting solids or room temperature liquids. Each of the low melting solids was confirmed to be a new solid phase, each of which is being further characterized. The liquids and solids are anticipated to be ILs, eutectics, or partially ionized systems, all of which will be suitable for the dissolution of f-element salts. Five new synthetic methodologies were developed to finding suitable crystallization conditions for f-element complexation with the goal of finding simple one pot reaction syntheses and crystallization strategies that could be used under the demanding conditions of transuranic chemistries. These synthetic methods yield many new crystalline phases which were studied by single crystal X-ray diffraction.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Flow instabilities in helical-coil steam generators for small modular reactors: A review

Here, this study covers the research and discoveries in two-phase flow-boiling instabilities available in the literature—specifically for a helical-coil steam generator (HCSG), including experimental findings, theoretical research, computational models, and system code analyses—supporting research and development of representative small modular reactors (SMRs). Like other new and advanced reactor systems, water-cooled SMRs require experimental data from both integral and separate thermal-hydraulics test facilities for the verification and validation (V&V) of the computational models and computer codes in order to design and obtain regulatory approval. The complex dynamics of two-phase flow-boiling instabilities includes flow regimes physics phenomena, flow-channel geometries, heat-transfer behavior, and interactions among the solid–liquid-gas within the system boundary, all of which are pivotal for understanding the design and operational challenges of SMRs. This study focuses on identifying the relevant knowledge gaps on boiling instabilities—specifically for a HCSG—and provides insights about future research direction optimizing the transport of thermal energy, mass-flow rates, and boundary conditions that ensure the adequate heat-transfer performance, operational stability, and safety associated with SMR systems.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2024

Durable and High-Performance SOECs Based on Proton Conductors for Hydrogen Production

Proton-conducting solid oxide electrolysis cells (P-SOECs) are a promising technology for cost-effective and efficient production of green hydrogen. Breakthroughs in materials development, optimization of cell structure, and achievement of high performance and durability are essential to significantly increase the commercial competitiveness of these technologies. The main objective of this project is to gain scientific knowledge for the rational design, fabrication, and demonstration of a robust, highly efficient, and low-cost SOEC technology based on a proton-conducting electrolyte membrane for hydrogen production. We focused on better understanding the degradation mechanisms of proton-conducting electrolytes, air electrodes, and catalyst materials under electrolysis mode to develop an effective strategy for rationalizing new materials that are vital for enhancing cell performance and durability. The scope includes enhancing the performance and durability of the electrolyte and electrode materials under realistic operating conditions, developing highly active and robust catalysts to minimize electrode losses while improving tolerance to contaminant poisoning, revealing the mechanism of enhanced activity and stability of the catalyst, and understanding the underlying degradation mechanisms. In addition, various characterization techniques were employed to gain a fundamental understanding of the materials’ behavior and their impact on cell performance, providing vital information to guide materials discovery and cell design. After defect chemistry engineering, the optimized donor and acceptor co-doped electrolytes BaMo/W 0.03 Ce 0.71 Yb 0.26 O 3-δ (BM/W03) showed substantially improved chemical stability against high concentrations of CO 2 and H 2 O compared to the state-of-the-art electrolyte (BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3-δ , BZCYYb1711) while maintaining comparable ionic conductivity and ionic transference number. To bypass the inherent trade-off between conductivity and chemical stability, we fabricated a bi-layer electrolyte composed of BZCYYb1711 coated with a highly-stable thin layer of BaHf 0.83 Yb 0.17 O 3-δ (BHYb). This bi-layer electrolyte displayed excellent chemical stability against high concentration CO 2 ; there was no detectable formation of BaCO 3 after exposure to 97% CO 2 (with 3% H 2 O) at 500 °C for 1000 hours and the rate of degradation in resistance was about 0.4% per 1,000 hours (kh). In contrast, the same BZCYYb1711 electrolyte without a BHYb coating degraded significantly under the same testing conditions; the degradation rate was increased to 5.1%/kh. In addition, a triple conducting air electrode Ba 0.9 Pr 0.1 Hf 0.1 Y0.1Co 0.8 O 3-δ (BPHYC) was developed by heavily doping transition metal ions into a proton-conducting material. This air electrode material, composed of 3 distinct phases, exhibits superior electrocatalytic activity due to the synergistic effect from the three component phases. Moreover, an active and durable catalyst, La 2 Ni 0.5 Fe 0.5 O 4+δ (LNF), was developed, showing excellent catalytic activity and contaminant tolerance, with a degradation rate of only 0.49%/kh when exposed to high concentrations of steam and Cr. Finally, single cells were constructed from the best electrolytes, electrodes, and catalyst coatings developed in this project. These cells demonstrated superior high current density at a given cell voltage, high roundtrip efficiency, and remarkable durability (up to 1000 hours of operation).

08 HYDROGEN↗
DOE OSTI2025

Accelerated data-driven materials science with the Materials Project

The Materials Project was launched formally in 2011 to drive materials discovery forwards through high-throughput computation and open data. More than a decade later, the Materials Project has become an indispensable tool used by more than 600,000 materials researchers around the world. This Perspective describes how the Materials Project, as a data platform and a software ecosystem, has helped to shape research in data-driven materials science. We cover how sustainable software and computational methods have accelerated materials design while becoming more open source and collaborative in nature. Next, we present cases where the Materials Project was used to understand and discover functional materials. We then describe our efforts to meet the needs of an expanding user base, through technical infrastructure updates ranging from data architecture and cloud resources to interactive web applications. Finally, we discuss opportunities to better aid the research community, with the vision that more accessible and easy-to-understand materials data will result in democratized materials knowledge and an increasingly collaborative community.

Horton, Matthew K↗
DOE OSTI2024

The Most Distant H i Galaxies Discovered by the 500 m Dish FAST

Abstract Neutral hydrogen (Hi) is the primary component of the cool interstellar medium (ISM) and is the reservoir of fuel for star formation. Owing to the sensitivity of existing radio telescopes, our understanding of the evolution of the ISM in galaxies remains limited, as it is based on only a few hundred galaxies detected in Hibeyond the local Universe. With the high sensitivity of the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we carried out a blind Hisearch, the FAST Ultra-Deep Survey, which extends to redshifts up to 0.42 and a sensitivity of 50μJy beam −1 . Here, we report the first discovery of six galaxies in Hi atz> 0.38. For these galaxies, the FAST angular resolution of ∼4′ corresponds to a mean linear size of ∼ 1.3 h 70 − 1 Mpc. These galaxies are among the most distant Hiemission detections known, with one having the most massive Hicontent ( 10 10.93 ± 0.04 h 70 − 2 M ⊙ ). Using recent data from the DESI survey and new observations with the Hale, Big Telescope Alt-azimuth, and Keck telescopes, optical counterparts are detected for all galaxies within the 3σpositional uncertainty ( 0.5 h 70 − 1 Mpc) and 200 km s −1 in recession velocity. Assuming that the dominant source of Hiis the identified optical counterpart, we find evidence of evolution in the Hicontent of galaxies over the last 4.2 Gyr. Our new high-redshift Higalaxy sample provides the opportunity to better investigate the evolution of cool gas in galaxies. A larger sample size in the future will allow us to refine our knowledge of the formation and evolution of galaxies.

Astronomy & Astrophysics↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗