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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 OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Cloverleaf Data Artifacts for ArtIMis LDRD

This report summarizes the use of the open-source CloverLeaf/CloverLeaf3D mini-apps to generate synthetic data sets to train foundation models for the ArtIMis LDRD DI. These data artifacts are intended to be used by LANL collaborators and shared externally with our university and institutional partners. Note that CloverLeaf/CloverLeaf3D is not a LANL simulation code.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of New Reactor Core Configuration for Power Uprate - Fuel Reload & Heat Processing Analyses, Core Design, System Safety Assessments, and Fuel Performance Analyses

With the passage of the Infrastructure Investment and Jobs Act in 2021 and the Inflation Reduction Act (IRA) in 2022, the United States stands at a critical juncture for the future of nuclear power. These landmark policies provide significant support for clean energy initiatives, positioning nuclear power as a key component of the nation’s strategy to reduce carbon emissions and achieve energy security. This growing emphasis on nuclear energy is driven by the need for reliable, low-carbon power sources as the country transitions away from fossil fuels. Federal policy, along with increasing state-level support, is encouraging investment in nuclear technology advancements to meet these demands. Building new nuclear power plants (NPPs), however, presents significant challenges due to high costs and long construction timelines. As a result, increasing the power output of existing NPPs through power uprates has emerged as a more feasible and cost-effective strategy. One key area of advancement is the development of accident-tolerant fuel (ATF), such as chromium-coated zirconium alloy cladding, which offers enhanced material performance, enabling power uprates in light water reactors (LWRs). Given the growing demand for nuclear energy fueled by federal policies and state initiatives, it is essential to evaluate the feasibility and benefits of significant power uprates in existing pressurized water reactors (PWRs) using advanced fuel technologies. The introduction of ATF concepts opens new opportunities for safely and economically achieving these power increases. Assessing whether these innovations can support substantial power uprates while maintaining operational safety is crucial to maximizing the potential of the nation’s existing nuclear infrastructure. This project aims to explore how power uprates can be achieved by boosting reactor thermal power output and optimizing reactor core design, while ensuring the safety and economic viability of NPPs. Specifically, it will focus on demonstrating the technical and economic feasibility of power uprates in a PWR using low 5-10% enrichment uranium (LEU+) high burnup (HBU) fuel combined with ATF concepts. In fiscal year 2024 (FY24), the research and development focus on building foundational models and conducting multi-physics performance and safety analyses to support the power uprate. The findings of the study would be shared through LWRS Seasonal Meetings, conferences and workshops with utility companies and researchers. These also serve as a basis for further study of fuel reloading optimization with ATF claddings.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

AI-based foundation models like FourCastNet, GraphCast are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions.

Lu, Dan↗
DOE OSTI2025

Electron-Proton Scattering Event Generation using Structured Tokenization

Recent work such as Omnijet-$\alpha$ has demonstrated that effective tokenization combined with transformer-based architectures can produce effective foundation models for jet physics. While tokenization may help models capture generalizable event characteristics, it also introduces discretization errors that may compromise the precision required for downstream physics analyses. As the number and complexity of the particle features grow, these errors are likely to grow proportionally. In this study, we investigate new tokenization strategies to improve the application of generative transformer models to \textsc{Pythia8} simulations of electron-proton scattering at the Electron-Ion Collider. Specifically, we propose a feature-based structured tokenization approach that utilizes multiple tokens per particle, improving expressivity, while reducing the total number of unique tokens needed. We evaluate this method against grid-based binning, K-means clustering, and vector-quantized variational auto-encoders on the event simulations. Our results show that feature-based structured tokenization reduces discretization error, leading to more accurate generative modeling of particle-level events.

Goldenberg, Steven [Thomas Jefferson National Acce↗
DOE OSTI2024

Early Career Researcher Support for Foundations of Molecular Modeling and Simulation Conference

This project enabled 14 early career researchers, including graduate students and postdocs, to attend the 8th triennial conference on Foundations of Molecular Modeling and Simulation (FOMMS 2022), which took place in Delavan, WI (Lake Geneva area), from July 17 to 21, 2022. Molecular-level modeling and simulation, which serve as critical tools for advancing science and engineering in a myriad of applications, were at the forefront of discussions. These ranged from the design of new materials for various energy applications to insights into the interaction between drug molecules and protein receptors within the body.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Leveraging AI and Spatial Data to Unlock Pipeline Integrity Insights: NETL’s Advanced Infrastructure Integrity Model (AIIM)

Maintaining the integrity of natural gas infrastructure plays a critical role in ensuring energy security. Robust, data-driven foundational AI models for pipeline integrity can help address risk management and mitigation issues. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. The AIIM dashboard was released in 2022 and utilizes multi-ML models for ensemble-type insights. It was expanded to include analytics on reported incidents. It was developed as an ESRI Dashboard to support data visualization & interrogation and contains pipeline data and model results.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2024

Evaluating the Use of Foundational Chemical Language Models in Multimodal Graph Fusion

Rapid and accurate prediction of the physicochemical properties of molecules given their structures remains a key challenge in cheminformatics. Machine learning approaches offer high-throughput options, but the optimality of inductive biases and data representations are up for debate. For example, BERT-based masked language models (MLMs) can be trained in a self-supervised way on hundreds of millions to billions of readily available SMILES strings. Another option is graph neural networks (GNNs), which can operate directly on molecular structures. Yet, generating accurate molecular geometry is computationally expensive, leading to a relative scarcity in data compared to SMILES strings. It is attractive to combine these two paradigms by pre-training an LM on a large corpus of SMILES strings and embedding these representation into a geometric graph neural network. Despite the promise of such an approach, and contrary to previous studies, we find mixed results with the combination of the LMs and GNNs on several molecule datasets. In particular, we found evidence for improvement on the FreeSolv and QM7 benchmarks, but degraded performance on the ESOL, LIPO and QM9 datasets compared to a GNN baseline.

Francel, Collin [University of Alabama]↗
DOE OSTI2025

Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

Advanced Infrastructure Integrity Model (AIIM)↗
DOE OSTI2026

Fidelity-preserving enhancement of ptychography with foundational text-to-image models

Ptychographic phase retrieval enables high-resolution imaging of complex samples but often suffers from artifacts such as grid pathology and multislice crosstalk, which degrade reconstructed images. We propose a plug-and-play (PnP) framework that integrates physics model-based phase retrieval with text-guided image editing using foundational diffusion models. By employing the alternating direction method of multipliers, our approach ensures consensus between data fidelity and artifact removal subproblems, maintaining physical consistency while enhancing image quality. Artifact removal is achieved using a text-guided diffusion image editing method (LEDITS++) with a pre-trained foundational diffusion model, allowing users to specify artifacts for removal in natural language. Demonstrations on simulated and experimental datasets show significant improvements in artifact suppression and structural fidelity, validated by metrics such as peak signal-to-noise ratio and diffraction pattern consistency. This work highlights the combination of text-guided generative models and model-based phase retrieval algorithms as a transferable and fidelity-preserving method for high-quality diffraction imaging.

image editing↗
DOE OSTI2026

Neural Scaling Laws for Jet Generation

Recently observed empirical scaling laws describe the performance of foundation-type models as three independent key quantities -- dataset size, compute, and model parameters -- are modified. Extracting these scaling laws informs the training of large complex models for which the tuning of hyperparameters in traditional ways is not feasible. This work for the first time explores if scaling laws can also be observed for the task of particle jet generation -- both relevant as a pre-training objective for foundation models and as in-situ simulation by itself. We indeed replicate the key logarithmic scaling law behavior for model-size scaling. Beyond studying the next token prediction validation loss of the generative model, we also study the sliced Wasserstein distance of five physical quantities that are not immediately available to the model during training. Our study shows that this quantity is monotonically related to the next token prediction validation loss, meaning that this loss is indeed a good proxy for the physics performance. For the scaling with dataset size and compute, we observe substantially weaker scaling behavior of both the loss and the sliced Wasserstein distance. We analyze this behavior by introducing the concept of a learnable window, and argue that autoregressive next token prediction on jet constituents exhibits comparatively rapid saturation relative to language-model studies. We discuss possible origins of this behavior, including the stochastic nature of QCD radiation and differences between generative and supervised learning tasks in collider physics.

Amram, Oz [Fermilab]↗
DOE OSTI2026

Improving Process Level Understanding of Boundary Layer Winds over the Northeast U.S. Shelf: The Third Wind Forecast Improvement Project (WFIP3)

The third Wind Forecast Improvement Project (WFIP3), a U.S. Department of Energy and National Oceanic and Atmospheric Administration sponsored investigation, sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the marine atmospheric boundary layer. WFIP3 focused on mesoscale and submesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high-use coastal zone, using a 3D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the marine atmospheric boundary layer over the ocean was done from an air-sea interaction flux tower and extended deployments of a large autonomous barge platform. Numerous critical forecasting phenomena were observed that are being evaluated within regional coupled and uncoupled modeling systems, including the National Oceanic and Atmospheric Administration's foundational High-Resolution Rapid Refresh forecast model.

Kirincich, Anthony↗
DOE OSTI2024

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence–based image segmentation model such as Meta’s Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Improving High Resolution Offshore Wind Resource Assessments and Forecasts using Observations in the MA/RI Lease Areas

The third Wind Forecast Improvement Project (WFIP3) sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the Marine Atmospheric Boundary Layer (MABL). WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high use coastal zone, using a 3-D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the MABL over the ocean was done from an air-sea interaction flux tower and extended deployments of a large barge platform. WFIP3 focused on mesoscale and sub-mesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. Numerous critical forecasting phenomena were observed, however the project was terminated prior to the completion of the field observational period and the analysis period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Calibration and validation of the foundation for a multiphase strength model for tin

In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory. The shear modulus model for the ambient phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress–strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston–Tonks–Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to ⁠10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid–liquid but never a fully liquid state, never fully lost strength, stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Calibration and validation of the foundation for a multiphase strength model for tin

In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a 10 multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory (DFT). The shear modulus model for the ambient β phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the β phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress-strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston-Tonks-Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to 10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability 20 of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid-liquid but never fully liquid state, never fully lost strength, 25 stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.

36 MATERIALS SCIENCE↗
DOE OSTI2023

IDAES-PSE 2.1.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. IDAES-PSE 2.1.0 Release Highlights New IDAES Examples Repository Starting with this release, the IDAES examples are developed in the new IDAES/examples repository. Along with many content and usability improvements, the most significant changes are: To install the examples, after installing IDAES, run pip install idaes-examples The idaes get-examples command, previously used for this, has been removed The HTML version is now available at https://idaes-examples.readthedocs.io The previous URL, https://idaes.github.io/examples-pse, will not be updated and may be removed at some point in the future For more details, refer to the resources available at IDAES/examples. Removal of Non-Functional Apps A review of the code in the idaes/apps and idaes/models_extra folders was undertaken, and a number of tools were identified as being outdated or non-functional and no longer supported by their development teams. Due to this, the following tools have been removed: idaes/apps/alamopy_depr (note that the new ALAMOpy interface remains avaialble in idaes/core/surrogates) idaes/apps/helmet idaes/apps/ripe idaes/apps/roundingRegression idaes/models_extra/carbon_capture Pyomo 6.6 This version of IDAES is the first requiring Pyomo 6.6. This version of Pyomo contains multiple internal improvements and refactorings. While for the majority of cases this should have positive or no impact on solvability of IDAES models, we are aware of a small number of models that have been affected as a result of these changes. For more information, refer to the Pyomo 6.6.1 release notes. Other highlights Model Initialization A prototype API for a new approach to initializing IDAES models is now available which makes available some new techniques for initializing models. This is documented in the Initializing Models Reference Guide Modular Properties Framework Support for some transport properties Helmholtz Equation of State properties Better error checking for case where unit models are set to include phase equilibrium but the property package is set to support only a single phase Multi-Stream Contactor model: a new base model for systems involving contacting of two or more streams with mass transfer. This model is intended to be used as the foundation for models such as membrane separators, solvent extraction and other similar processes. This is documented in the Multi-Stream Contactor Reference Guide idaes/models_extra/power_generation report() methods for unit models using Helmholtz equation of state General Code Maintenance Streamlining of dependencies and creation of new optional dependency groupings to support non-core tools General linting of codebase to ensure compliance with most pylint checks Spell checking of all code and doc strings Removal of backward compatibility code for Python 2

IDAES↗