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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2026

“One Table to Rule Them All”: How a Single Table can Enable Extensive Insights, Analytics and Assessment on Human Mobility Data

While much research has been conducted in Human Mobility Science, most studies on the analytics/insights part generally focus on one of the following: processing and analytics on human stop-trip behavior, design of individual mobility metrics (often in silos), calculation and characterization of only a handful (typically 5-6) of human mobility metrics on geospatial-temporal human mobility data of interest. Although human mobility research offers a vast and diverse array of available metrics, most individual studies typically compute only a small subset of five or six metrics at a time when analyzing trajectory datasets of human mobility across different areas of interest. This paper is motivated by the critical need to repeatedly compute an extensive array of human mobility metrics across several trajectory datasets and perform individual metric-level benchmarking to establish a new, standardized Test and Evaluation (T&E) suite for the field of Human Mobility Science. We first present our findings on the minimal yet sufficient pre-processing required to reliably and efficiently compute a wide range of human mobility metrics. The key findings are specifically related to the proposed Composite Stop Locations table, which serves as a core pre-processing data layer. Subsequently, we present a case study demonstrating how the Composite Stop Locations table facilitates computation of at least 14 distinct human mobility metrics (unlike 5-6 different set of metrics used for studies in the literature) using the popular and open-source OpenPFLOW dataset. Finally, we have also presented an example of our benchmarking methodology to evaluate the quality and performance of the trajectory dataset of interest, assessed across multiple human mobility metrics.

De, Debraj [ORNL] (ORCID:0000000233630020)↗
DOE OSTI2026

MOSAIC-CONUS: A Multimodal, Multi-Temporally Paired Dataset for Earth Sciences

Earth embeddings—vector representations of geographic locations indexed in space and time—are emerging as a unifying interface for geospatial AI. However, their quality depends not only on model design, but on how multimodal Earth observation (EO) data are spatially indexed, temporally aligned, and cross-modally associated during pretraining. We introduce MOSAIC-CONUS (Multimodal Observations with Spatially Aligned Imagery, Urban Points of Interest, In-Situ Measurements and Text Captions), a large-scale EO dataset over the contiguous United States, organized around 250,000 stratified point indices that serve as stable spatial keys across seven modalities: active radar, passive optical imagery, lidar-derived elevation, land cover, functional context, hydrometeorological measurements, and textual summaries. Unlike existing EO datasets, MOSAIC-CONUS introduces four contributions not jointly addressed in prior work: 1. an open-source, large-scale multimodal EO corpus structured around point-indexed data designed to support Earth embedding learning; 2. explicit radar-optical pairing tables spanning twelve temporal alignment regimes, formalizing cross-sensor alignment as a controllable variable for analyzing how temporal mismatch across modalities influences learned embeddings quality; 3. a benchmark suite spanning cross-modal retrieval, annual nightlights regression, and basin-held-out streamflow prediction, positioning MOSAIC-CONUS as a benchmark-ready resource for multimodal AI systems; and 4. a language-based embedding layer through co-registered textual summaries, enabling Earth embeddings to function as a queryable interface for agentic AI systems. The dataset and pairing protocols are publicly released.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Superatoms as Superior Catalysts: ZrO versus Pd

Abstract Single‐atom catalysts are the focus of studies for over a decade due to their enhanced reactivity at smaller sizes. However, they have limitations as they offer only one active site, which may not be sufficient for reactions requiring the co‐adsorption of multiple reactants. Additionally, atoms can migrate on a substrate and coalesce, resulting in decreased reactivity. Here, an alternate path, a single‐superatom catalyst is provided. Superatoms are clusters of atoms that mimic the chemistry of atoms even if they do not contain a single atom whose chemistry they mimic. Motivated by an experimental paper on the photoelectron‐spectroscopy of negatively charged ions where ZrO is found to mimic properties of a Pd atom, first the reaction of Pd and ZrO with small molecules in the gas‐phase is studied and found that ZrO not only mimics the chemistry of Pd, but is able to activate these molecules more strongly than Pd. A detailed first‐principles study of CO 2 reduction (CO 2 ‐RR) and hydrogen evolution reactions (HER) on Pd and ZrO supported on graphene, Au(111), and Cu(111) surfaces shows that superatoms are indeed superior catalysts. The ability to design numerous superatoms by varying size and composition offers a promising new paradigm for catalyst design and synthesis.

Chemistry↗
DOE OSTI2024

Determining reference standard strength for neutron-irradiated reduced activation ferritic/martensitic steel F82H by Bayesian method

The deterministic approach widely adopted in the design of structural components relies on systematically defined design limits using empirically determined safety factors. However, this approach is not always appropriate because structures are subjected to a variety of loads in the practical environment, which may result in excessively conservative design limits. In recent years, a more rigorous probabilistic approach that incorporates material strength distributions has become an important solution. In the probabilistic approach, the probability density functions of material strength properties underpin the design criteria. Here, the objective of this study is to identify the density distribution functions that best describe tensile properties of irradiated F82H to define a reference strength for DEMO design. Due to the limited number of existing data, this study specifically employs a Bayesian prediction method based on Monte Carlo simulations to determine a material reference value with statistical reliability and to investigate its effectiveness. For example, the dependence of tensile properties of 300 °C irradiated materials on irradiation damage and the range predicted by 95% Bayesian estimation was evaluated. As a statistical model for the dose dependence of statistical parameters, the normal distribution exhibited a better fit for 0.2% proof strength and tensile strength, whereas the distribution of total elongation data gave comparable reference values for both the normal and Weibull distribution models. Both models gave comparable criteria for the distribution of total elongation data. The Weibull model also gave better results for uniform elongation. The function best describing the model was a logarithmic law for both 0.2% proof strength and tensile strength, while a power law for both total and uniform elongation, which allowed for more comprehensive data prediction of irradiation data with statistical accuracy for DEMO reactor design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of Co on twin formation and magnetic properties of Sm(Fe,Ti,V) 12 alloys

Transferring the excellent intrinsic magnetic properties of SmFe 12 -based compounds to their extrinsic properties remains the main challenge in the development of high-performance SmFe12-based permanent magnets. Twin formation is one of the reasons for the inability to achieve high coercivity and remanence. Here we have shown that the addition of Co in Sm(Fe 1-x Co x ) 10–11 M 1–2 alloys, where M=Ti and V, leads to an increase in twin density. Microstructural characterizations revealed that the atomic arrangement in the twin boundary changes depending on the stabilizing element, which directly influences the local intrinsic magnetic properties. Theoretical investigations showed that the critical grain size at which twin formation can be hindered by grain size reduction decreases when the stabilizer changes from V to Ti. Furthermore, this study shows that the alloy composition influences not only the intrinsic magnetic properties but also the twin formation energy and its grain size dependence, crucial for the design of SmFe12-based permanent magnets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

In Silico Design of Methyl-Driven Overhauser Dynamic Nuclear Polarization Agents

Overhauser effect (OE) dynamic nuclear polarization (DNP) has drawn attention owing to its enhanced performance at ultrahigh magnetic fields. The lack of design principles has, nevertheless, limited the development of OE polarizing agents when compared to those used for cross-effect DNP. Here, we measured the 19 F OE DNP performance of a series of CF 3 -functionalized Blatter-type radicals. Using density functional theory calculations, we accurately predict the methyl-driven OE performance, paving the way for computer-aided design of optimized OE DNP polarizing agents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Performance characterization of x-ray crystal spectroscopy highly oriented pyrolytic graphite reflectors based on x-ray diffractometry experiments

The use of Highly Oriented Pyrolytic Graphite (HOPG) reflectors is often proposed in the design of X-ray Crystal Spectroscopy (XCS) diagnostic systems for the next-generation tokamak devices, including the ITER project. Here, this study introduces an experimental study based on the X-Ray Diffractometry (XRD) method to evaluate the performance of HOPG reflectors. The experimental method provides both the angular responses and the reflectivities of the HOPG reflectors. A demonstrative XRD experiment is conducted, and the details of the experiment are introduced. This method enables precise studies on HOPG reflectors, facilitating the design of XCS diagnostic systems for future tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Development of advanced vacuum technologies for extending plasma pulse duration on EAST

Advanced vacuum technologies, including pumping, fueling and wall conditioning, have been successfully developed or upgraded to efficiently control the fuel and impurity particles to extend the plasma pulse duration in the experimental advanced superconducting tokamak (EAST). To improve the particle exhaust rate cryopumps with a 60% increase in pumping speed and ∼2 times increase in saturation capacity have been developed, and molecular pumps with a ∼30% increase in pumping speed have been upgraded. In order to monitor the molecular pump status while avoiding bearing faults and overload accidents, a fault detection system has been built which can offer an early warning to avoid more losses within the fusion device. A series of fueling technologies have been developed including gas injection system, supersonic molecular beam injector, pellet injector (PI), massive gas injector and shattered pellet injector, installed at the midplane and divertor positions at different ports to improve fueling uniformity and efficiency. Meanwhile, routine wall conditioning such as electric and hot N 2 baking, ion cyclotron wall conditioning and glow discharge cleaning have been successfully developed to remove the impurity particles from the inner component and materials. The low Z material wall coating and real-time powder injection during plasma discharge are also designed and applied to further improve particle control capability. Finally, by using these advanced vacuum related technologies, good vacuum (<2 × 10 −6 Pa) and wall conditions are realized, and the fuel and impurity particles can be effectively and stably controlled, which promotes the achievement of the record plasma of ∼1056 s pulse duration with the line-averaged electron density of 1.8 × 10 19 m −3 on EAST. They provide a very important reference for vacuum system design and operation for future fusion devices.

EAST↗
DOE OSTI2024

Experimental and Analytical Verification of ASME Section IiII Division 5 Creep-Fatigue Design Rules

The continuous advancement of structural materials and the growing demands for more reliable and economical structural components in high-temperature reactor applications have necessitated the development of comprehensive design methodologies and design rules. Mechanical degradation of structural components at elevated temperatures subjected to cyclic deformation is controlled by the creep-fatigue damage. Over the past few decades, diligent research efforts have been dedicated to refining the development of elevated temperature design rules in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section III, Division 5 and to develop conservative design rules that can effectively guard against the risk of creep-fatigue failure. In ASME Section III, Division 5, for a design to pass the creep-fatigue acceptance criteria, creep damage and fatigue damage are evaluated separately, and these damages must not violate the bi-linear creep-fatigue interaction diagram, i.e., the so-called D-diagram. The creep-fatigue damage evaluation procedure assumes that the effects of the actual cyclic loading sequence can be bounded by assuming that the individual loading cycles are uniformly distributed throughout the component design life. In this study, creep-fatigue experiments with variable amplitudes and loading sequencies were designed and performed on Alloy 617 at high temperatures. The results were analyzed to evaluate the loading history effect on creep-fatigue damage accumulation and to verify the assumptions for the creep-fatigue evaluation design rules.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Inverse design of cellular structures with the targeted nonlinear mechanical response

Advanced additive manufacturing capabilities have enabled a transformational ability to create sophisticated cellular structures using diverse materials. By altering the topology of the unit cell, the mechanical behavior, such as the stress-strain response during compression, can be modulated. Nevertheless, identifying a printable topology within an enormous design space that would precisely deliver the targeted nonlinear material response is challenging. We propose a data-driven generative framework based on a conditional variational autoencoder (cVAE) architecture that can inverse design the cellular structure based on the intended nonlinear stress-strain response. Trained on a dataset of structure-property pairs, the cVAE learns a compact and expressive latent space that enables efficient mapping from targets to feasible geometries. Two inference modes are explored: (1) decoder-only generation, which enables the exploration of diverse designs conditioned solely on the desired mechanical response, and (2) encoder-decoder generation, which further allows for the incorporation of desired topologies, ensuring the generated structure conforms to both mechanical properties and to desired-topology constraints. The results demonstrate that the model can generate structurally plausible and mechanically accurate designs, with the predicted stress-strain curves closely matching the targets. Even under joint conditioning, the model effectively balances geometric fidelity and functional performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From Data to Discovery: AI's Transformative Role in Thin Film Research

The advancement of thin film technologies is pivotal for progress in numerous fields, including energy, electronics, and quantum computing. However, the traditional trial-and-error approach to materials discovery is inherently slow and inefficient. This presentation will showcase how artificial intelligence (AI) is transforming thin film research by enabling a data-driven paradigm shift. We will highlight our past successes in applying AI to understand radiation damage in thin film oxides, demonstrating how graph analytics can unravel complex material behavior. Additionally, we will provide insights into our current work at the National Renewable Energy Laboratory, where we are leading the charge in autonomous materials science. Backed by a $14M investment in our characterization facility, we are developing AI-guided workflows that seamlessly integrate experimentation and AI-guided decision-making. By harnessing the power of AI, we aim to accelerate the discovery and design of high-performance thin films, propelling innovation across a multitude of industries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The ePIC Simulation Campaign Workflow on the Open Science Grid

The ePIC collaboration is realizing the first experiment of the future Electron-Ion Collider (EIC) at the Brookhaven National Laboratory that will allow for a precision study of the nucleons and the nucleus at the scale of sea quarks and gluons through the study of electron-proton/ion collisions. This paper will discuss the current workflow for running centralized simulation campaigns for ePIC on the Open Science Grid (OSG) infrastructure. This involves monthly releases of ePIC software and container deployments to CVMFS, generation of input datasets in HepMC format according to collaboration-defined policy, using Snakemake in CI/CD for validation and benchmarking, and submitting jobs to the OSG condor scheduler for opportunistic running on available resources. File transfers utilize XrootD, and Rucio is used for data management. The workflow is continuously refined to improve daily throughput (currently 50-100k core hours per day) and minimize job failures. Since May 2023, monthly simulation campaigns employing the workflow have cumulatively used over 20 million core hours on the OSG and produced over 350 TB of simulation data. The campaigns incorporate simulations for the broad science program of the EIC and are actively used for the detector and physics studies in preparation of the Technical Design Report (TDR).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

From breaking rules to making rules in materials science

This editorial is a perspective article discussing the broader philosophy of synthesis science, emphasizing how techniques such as MBE allow researchers to manipulate bonding, structure, and defects beyond equilibrium thermodynamics, enabling the design of new materials and emergent properties through controlled growth and epitaxial engineering.

Jalan, Bharat [Univ. of Minnesota, Minneapolis, MN↗
DOE OSTI2024

Developing and Running Quantum Algorithms for Chemistry and Materials (QAChMat) (Final Technical Report)

The goal of the project is to design, develop, and execute new computational methods on practical quantum computing platforms to simulate hard problems in chemical and materials sciences. We exploited the two leading quantum computing platforms of trapped atomic ion and superconducting qubits, established in laboratories at Duke University and the University of Maryland, to discover new simulation and computational methods for the study of quantum chemistry and materials.

36 MATERIALS SCIENCE↗