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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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Code for plots, models, data generation and other utilities relating to the paper "wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation" https://arxiv.org/abs/2511.05615 [FERMILAB-PUB-25-0359-CSAID]
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.
High-entropy ceramics have garnered interest due to their remarkable hardness, compressive strength, thermal stability, and fracture toughness; yet the discovery of new high-entropy ceramics (out of a tremendous number of possible elemental permutations) still largely requires costly, inefficient, trial-and-error experimental and computational approaches. The entropy forming ability (EFA) factor was recently proposed as a computational descriptor that positively correlates with the likelihood that a 5-metal high-entropy carbide (HECs) will form the desired single phase, homogeneous solid solution; however, discovery of new compositions is computationally expensive. If you consider 8 candidate metals, the HEC EFA approach uses 49 optimizations for each of the 56 unique 5-metal carbides, requiring a total of 2744 costly density functional theory calculations. Here, we describe an orders-of-magnitude more efficient active learning (AL) approach for identifying novel HECs. To begin, we compared numerous methods for generating composition-based feature vectors (e.g., magpie and mat2vec), deployed an ensemble of machine learning (ML) models to generate an average and distribution of predictions, and then utilized the distribution as an uncertainty. Here we then deployed an AL approach to extract new training data points where the ensemble of ML models predicted a high EFA value or was uncertain of the prediction. Our approach has the combined benefit of decreasing the amount of training data required to reach acceptable prediction qualities and biases the predictions toward identifying HECs with the desired high EFA values, which are tentatively correlated with the formation of single phase HECs. Using this approach, we increased the number of 5-metal carbides screened from 56 to 15,504, revealing 4 compositions with record-high EFA values that were previously unreported in the literature. Our AL framework is also generalizable and could be modified to rationally predict optimized candidate materials/combinations with a wide range of desired properties (e.g., mechanical stability, thermal conductivity).
Core-collapse supernovae are intense sources of tens-of-MeV neutrinos. However, there is no experimental data to validate the current event generator MARLEY (Model of Argon Reaction Low Energy Yields) that can model supernova neutrinos. To validate the model, I developed a new muon capture feature within the MARLEY simulation framework by coding key functions in C++, Python, and ROOT. I generated and analyzed one million simulated muon capture events and comparing the results to experimental data. To improve the model, I worked on optimizing the model’s parameters to improve its precision using statistical methods.
This paper presents a microstructure-based model for low cycle fatigue (LCF) behavior and life of Nickel-based alloy Hastelloy X manufactured using laser-powder bed fusion (L-PBF) additive manufacturing (AM). AM Hastelloy X, a solution-strengthened alloy, is tested at elevated temperature under fully reversed LCF conditions at different strain levels. A generalized plane strain finite element model is generated from electron backscatter diffraction (EBSD) characterization. The constitutive behavior of the material under fatigue is modeled using crystal plasticity and calibrated with both monotonic tensile and cyclic stress–strain data. The fatigue micro-crack initiation and propagation in the microstructure is modeled using a modified Chaboche fatigue damage model. An embedded boundary condition with a homogenous medium is used to apply the cyclic deformation and prevent numerically introduced over-constraints during fatigue simulation. A ‘cycle-jump’ method is used to accelerate the fatigue simulation and reduce the computational cost. The simulation results are compared to LCF experiments, showing satisfactory matches in cyclic stress behavior and number of cycles to macro-crack initiation for all applied strain ranges. In addition, the model illustrates the potential for quantifying microscale fatigue life impacting factors such as microstructure and surface roughness, which is needed to accurately quantify the reliability of AM components in service.
This project, funded by DOE Atmospheric System Research (DE-SC0020050), focused on improving the representation of mesoscale convective systems (MCSs) in numerical weather and climate models by leveraging high-resolution observations from the DOE Atmospheric Radiation Measurement (ARM) program. The research aimed to evaluate model sensitivities to grid spacing, microphysics, and planetary boundary layer (PBL) schemes, with a particular emphasis on improving convection parameterization for high-resolution modeling. Findings from this work highlight several key advancements. Model validation against ARM radar wind profiler data from the Southern Great Plains (SGP) and Manaus (MAO) sites revealed systematic biases in simulated convective mass flux profiles, leading to the development of an observationally constrained evaluation framework for diagnosing and improving model performance. Sensitivity analyses demonstrated that the representation of Amazonian MCSs was highly dependent on PBL scheme selection, while mid-latitude MCSs were more strongly influenced by microphysics parameterizations. A series of high-resolution WRF simulations, ranging from 4 km to 125 m grid spacing, provided insight into the behavior of convective drafts across scales. While updraft properties converged at sub-kilometer resolutions, biases in downdraft intensity persisted even at the finest resolution tested, emphasizing the need for further refinements in model physics. Additionally, comparisons of MCS vertical structures between mid-latitude and tropical environments revealed stronger updrafts and larger mass flux in mid-latitude MCSs, providing critical insights for improving climate model representations of storm-scale dynamics. The project’s findings have already contributed to advancing numerical modeling capabilities, particularly in WRF, MPAS, ICON, and DOE’s SCREAM model, by refining how convective processes are represented in high-resolution climate simulations. Results were disseminated through peer-reviewed publications, conference presentations, and ARM/ASR Research Highlights, engaging the broader scientific community. The project also provided valuable training opportunities for two postdoctoral researchers, who played central roles in model development, analysis, and dissemination of results. Their work contributed to several publications and conference presentations, helping prepare them for careers in atmospheric modeling. By improving the simulation of MCSs, this research directly supports the development of next-generation climate models capable of more accurately representing extreme precipitation and convective processes. The insights gained will inform future improvements in convective parameterization and guide the design of high-resolution weather and climate simulations, ultimately enhancing the reliability of climate projections and weather forecasts.
As large language models scale to trillions of parameters, their computational and memory requirements present critical challenges for efficient training and deployment. While Mixture of Experts (MoE) architectures enable efficient scaling through sparse parameter activation, and state-space models like Mamba offer linear-time complexity, principled methods for combining these paradigms remain undeveloped. We introduce HARMONY (Hybrid Architecture Research for Mamba, Optimized with Neural efficiencY), a multi-objective evolutionary neural architecture search framework for discovering efficient hybrid language models that integrate Transformer attention mechanisms, Mixture-of-Experts routing, and Mamba state-space components. Through large-scale distributed search using 16,384 MI250X GPUs on the Frontier supercomputer, HARMONY explores a comprehensive design space encompassing six attention variants (MHA, MQA, GQA, MLA, SWA, and Mamba-2), variable MoE configurations with both routed and shared experts, and extensive Mamba hyperparameters. Our framework discovers heterogeneous architectures that balance training performance with computational efficiency through multi-objective optimization incorporating latency penalties and fitness-based selection. Analysis of discovered architectures reveals that optimal hybrid designs favor heterogeneous component mixing rather than homogeneous patterns, with Mamba-2 and Multi-Head Latent Attention (MLA) emerging as preferred mechanisms. Discovered architectures demonstrate superior training efficiency: our best configuration achieves a final perplexity of 1.0874 with 2.38B parameters while processing 4,320 tokens/second, outperforming significantly larger manually designed models. Full-scale evaluation shows HARMONY's top architectures achieve better loss trajectories than equivalently-sized models using state-of-the-art configurations including Mixtral, Jamba, and Samba. Additionally, we demonstrate 91% weak scaling efficiency when training discovered 36B-parameter models across 1,024 GPUs. HARMONY is released as an open framework with comprehensive tools for building and training hybrid models using expert-data-pipeline parallelism, democratizing access to automated architecture design for next-generation language models.
Neutron scattering experiments have played vital roles in exploring materials properties in the past decades. While user interfaces have been improved over time, neutron scattering experiments still require specific knowledge or training by an expert due to the complexity of such advanced instrumentation and the limited number of experiments each person may perform each year. This paper introduces an innovative chatbot application that leverages Large Language Models(LLM) and Retrieval-Augmented Generation (RAG) technologies to significantly enhance the user experience at the EQ-SANS, a small-angle neutron scattering instrument at the Spallation Neutron Source of Oak Ridge National Laboratory. Through a user-centric design approach, the EQ-SANS Assisting Chatbot (ESAC) serves as an interactive reference for users, thereby facilitating the use of the instrument by visiting scientists. By bridging the gap between the users of EQ-SANS and the control systems required to perform their experiments, the ESAC sets a new standard for interactive learning and support for the scientific community using large-scale scientific facilities.
Motivation Multiple deep learning model architectures can be used to segment bacterial membranes in cryoEM images. However, an AI-based tool advancement is often presented with only a single segmentation model for broad use, and this single model may show inconsistent results across datasets from different users. Here, we present the Top Model Decision Tree, a model screening framework to screen for the best model to generate bacterial inner and outer membrane masks based on user priorities. We use pre-trained segmentation models from YOLOv11, YOLO26, U-Net, Detectron2 and SAM3 fine-tuned on bacterial inner and outer membranes imaged with cryoEM. Run the Framework This notebook must be opened in Google Colab. Mount Google Drive and run with a GPU-based runtime. Open the notebook and follow steps to git clone in folders and files within this repository. There will be a repeating top_model_decision_tree.ipynb (notebook clone) that will not be used. Save your .png binary mask files and .csv table outputs within your Google Drive or download before closing the notebook. The models and all analysis/training scripts are available at [GitHub: https://github.com/Lynnicia/CryoEM_membranes_top_model_decision_tree and https://github.com/Sireesiru/Semantic-Segmentation-of-bacterial-cell-envelope-using-U-Nets.
MS Student Naveen Gupta worked on the problem of full waveform inversion (FWI) using neural networks as shown in Figure 1. Our goal was to learn a neural network to represent the subsurface velocity model, which when fed into the FWI module (implemented using a numerical forward model of wave equations) produces amplitude estimates that match with ground-truth observations of amplitude. We used neural networks to solve the inverse problem of estimating velocity distributions for a given seismic amplitude data such that, once trained, our neural network model can generate a distribution of velocity profiles for different random vectors fed as inputs to the neural network model.
NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the publication “Refining the Hydrogeologic Framework of a Large River Corridor Model Using Waterborne Transient Electromagnetics” submitted to Water Resources Research (Terry et al. 2025). The data package contains the groundwater modeling dataset from PFLOTRAN software. It includes the python script for mesh generation, boundary condition setting, PFLOTRAN input deck formation and postprocessing. It couples groundwater flow and species transport for Hanford Reach river corridor and pipelines the model generation and processing. This model can be used to easily generate the model and analysis for Hanford site. It can also be adjusted to other hydrologic area with ease. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. The data package consists of 6 folders: (1) “data” contains all necessary data as input and intermediate data for processing; (2) “mesh” contains all mesh related files to generate mesh in Hanford Reach river corridor; (3) “model_run” contains the generated script for PFLOTRAN modeling; (4) “notebooks” contains all the Python script to generate the model; (5) “output” contains all the output from the computation; (6) “postprocessing” contains the Python script to generate scientific figure for manuscript. All files are .csv (comma-separated values), .h5 (HDF5 format), .in (input files), .ipynb (Jupyter notebooks), .p (Python pickle), .png (images), .PNG (images), .py (Python scripts), .pyc (Python bytecode), .r (R scripts), .sh (shell scripts), .txt (text files), .vtu (3D mesh/visualization format), .xz (compressed archive), or .zip (compressed archive).
We propose a three-Higgs-doublet model (3HDM) that goes beyond natural flavor conservation and in which each of the three Higgs doublets couples mainly to a single generation of fermions via nonstandard Yukawa structures. A hierarchy in the vacuum expectation values of the three Higgs doublets can partially address the Standard Model flavor puzzle. In light of the experimentally observed 125 GeV Higgs boson, we primarily work within a 3HDM alignment limit such that a Standard Model-like Higgs is recovered. In order to reproduce the observed Cabibbo–Kobayashi–Maskawa mixing among quarks, the neutral Higgs bosons of the theory necessarily mediate flavor changing neutral currents at the tree level. We consider constraints from neutral kaon, B meson, and D meson mixing as well as from the rare leptonic decays B s / B 0 / K L → μ + μ − / e + e − . We identify regions of parameter space in which the new physics Higgs bosons can be as light as a TeV or even lighter. Published by the American Physical Society 2025
As hybrid power plants (HPPs), such as photovoltaic (PV) and battery combinations, become increasingly important in power systems with high renewable energy penetration to address PV variability and ensure grid stability. This paper focuses on the urgent need to model the coordination between PV and battery systems in HPPs while accounting for battery degradation. We present a generation scheduling model that explicitly incorporates PV-battery hybridization in the unit commitment problem. Moreover, the cost function of the HPP scheduling problem endogenously considers battery degradation with adjustable weights to strike a balance between minimizing production costs and prolonging battery life, particularly when providing energy arbitrage and ancillary services. Using a realistic island system simulation, we demonstrate that accounting for battery degradation in the scheduling problem can significantly extend battery life with only minor additional production costs.
Argonne National Laboratory (the Contractor), located in Lemont IL, and Oklo, Inc., (the Participant), headquartered in Santa Clara, CA, propose to enter into a Cooperative Research and Development Agreement (CRADA) to perform a gap analysis of thermal hydraulic data, perform prototypical fuel assembly pressure drop and cavitation model validation, generate the experimental data as well as the corresponding uncertainties for this matrix and, finally, develop the validation models with the Argonne system level code SAS4A/SASSYS-1, the Argonne subchannel analysis code DASSH, the Argonne high fidelity code Nek5000, and/or the Idaho National Laboratory code Pronghorn Subchannel. The work outlined below will significantly improve the experimental and validation database currently available for liquid metal fast reactors, thus making it a viable part of a comprehensive reactor design and licensing suite to be used by the participant.
The detection, location, and monitoring of underground structures are of great importance to national and global security. Tunnels and voids generate seismic signatures detectable at the surface, but using non-invasive seismic data to image near-surface presents several challenges in real-world applications. In this report, we describe the use of a dense surface seismic deployment to generate subsurface models of the Graymont Pleasant Gap mine - a single-layer mine with a complex structure embedded in a high-velocity P-wave limestone bedrock. Our approach consists of three key methods. We use P-wave arrival times from local blast events to perform a tomography inversion with the tomoTD method, constructing a P-wave velocity model of the subsurface. We model the layer above the mine using Rayleigh wave ellipticity and inversion techniques. We leverage ongoing anthropogenic activities to identify and locate noise sources both on the surface and within the subsurface. With this integrated approach we aim to overcome the challenges and enhance our ability to non-invasively characterize underground structures, contributing to improved seismic monitoring techniques.
Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon (a-C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a-C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.
While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.
Abstract Most current land models approximate terrestrial hydrological processes as one‐dimensional vertical flow, neglecting lateral water movement from ridges to valleys. Such lateral flow is fundamental at catchment scales and becomes crucial for finer‐scale land models. To test the effect of incorporating lateral flow toward three‐dimensional representations of hydrological processes in the next generation land models, we integrate a water tracer model into the WRF‐Hydro framework to track water movement from precipitation to discharge and evapotranspiration. This hydrologic‐tracer integrated system allows us to identify the key mechanisms by which lateral flow affects the flow paths and transit times in WRF‐Hydro. By comparing modeling experiments with and without lateral routing in two contrasting catchments, we determine the impacts of lateral flow on the transit times of precipitation event‐water. Results show that with limited hydrologic connectivity, lateral flow extends the transit times by reducing (increasing) event‐water drainage loss (accumulation) in ridges (valleys) and allowing reinfiltration of infiltration‐excess flow, which is missing in most land models. On the contrary with high hydrologic connectivity, lateral flow can effectively accelerate the water release to streams and reduce the transit time. However, the transit times are substantially underestimated by the model compared with isotope‐derived estimates, indicating model limitations in representing flow paths and transit times. This study provides some insights on the fundamental differences in terrestrial hydrology simulated by land models with and without lateral flow representation.