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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Predicting the rheology of limestone calcined clay cements (LC 3 ): Linking composition and hydration kinetics to yield stress through Machine Learning

The physicochemical characteristics of calcined clay influence yield stress of limestone calcined clay cements (LC 3 ), but the independent influences the clay's physical and chemical characteristics as well as the effect of other variables on LC 3 rheology are less well-understood. Further, a relationship between LC 3 hydration kinetics and yield stress – important for informing mixture design – has not yet been established. Here, rheological properties were determined in pastes with varying water-to-solid ratio (w/s), constituent mass ratios (PC:metakaolin:limestone), limestone particle size and gypsum content. From these data, an ML model developed allowed the independent examination of the different mechanisms by which metakaolin fraction influences yield stress of LC 3 , identifying four predictors – packing index, Al 2 O 3 /SO 3 , total particle density and metakaolin fraction relative to limestone (MK/LS) – most significant for predicting LC 3 yield stress. A methodology based on kernel smoothing also identified hydration kinetics parameters best correlated with yield stress.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The ePIC dual-radiator RICH detector

The dual radiator Ring Imaging Cherenkov (dRICH) detector is required to provide continuous hadron identification from ≈3 GeV/c up to ≈50 GeV/c, and to supplement electron and positron identification from a few hundred MeV/c up to about 15 GeV/c, in the forward (ion-side) end-cap of the ePIC experiment. Such an extended momentum range imposes the use of two radiators, gas and aerogel. The common imaging system, that ensures compactness and cost-effectiveness, is based on SiPM sensors to work in a high non-uniform magnetic field. During the R&D phase, the dual radiator principle and the single component performance have been validated. A status overview of the project is presented. The design and technological choices are discussed together with the results obtained from laboratory characterization of the component demonstrators and beam tests of the evolving prototypes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Decision Science for Machine Learning (DeSciML)

The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring rely on machine learning for aspects of their analyses. Likewise, future technologies, such as incorporation of data-driven methods into the stockpile surveillance and predictive failure analysis for weapons components, will all rely on decision-making that incorporates the output of machine learning models. In this project, our main focus was the development of decision scientific methods that combine uncertainty estimates for machine learning predictions, with a domain-specific model of error costs. Other focus areas include uncertainty measurement in ML predictions, designing decision rules using multiobjecive optimization, the value of uncertainty reduction, and decision-tailored uncertainty quantification for probability estimates. By laying foundations for rigorous decision making based on the predictions of machine learning models, these approaches are directly relevant to every national security mission that applies, or will apply, machine learning to data, most of which entail some decision context.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Role of the Character of the Excited State of Singly Reduced Rh 2 (II,II) Intermediates on Photocatalytic Activity

Singly reduced intermediates have recently been implicated as photoactive intermediates in a number of important reactions; however, their photophysical properties remain poorly understood. A series of dirhodium(II,II) complexes, cis-[Rh 2 (p-R-Form) 2 (bncn) 2 ] 2+ (bncn = benzo[c]cinnoline; p-R-Form = N,N′-di-p-R-phenylformamidinate), where R = −OCH 3 (1), −CH 3 (2), −H (3), −F (4), −Cl (5), and −CF 3 (6), and their respective radical anions were prepared and their excited state properties were investigated. Complex 3 acted as a singlemolecule photocatalyst for H 2 production with red light. Substitution on the formamidinate ligands in 1−6 affected the energy of the Rh 2 (δ*)/Form(π,nb) highest occupied molecular orbital (HOMO), consistent with the metal/ligand-to-ligand charge transfer ( 1 ML-LCT) absorption maxima and the 3 ML-LCT excited state lifetime, ranging from 1.6 ns in 1 to 54 ns in 6 in CH 3 CN. The highest turnover number for photocatalytic H 2 evolution was observed for 3, and the lowest values were for 1 and 6. The radical anion, [Rh 2 ] − , formed during photocatalysis, was shown to absorb a photon and undergo a second reduction. The lifetimes of the doublet excited states of [3] − and [6] − were 0.49 and 0.24 ns, respectively. Calculations showed that the lowest energy excited state in [3] − was 2 ML-LCT, whereas that in [6] − was a bncn − → Rh 2 (σ*) ligand-to-metal charge transfer ( 2 LMCT) state. The 2 LMCT state stabilized across the series from [1] − to [6] − , pointing at its role in modulating the photophysical properties. This work highlights the importance of the reductive quenching of [Rh 2 ] − and the generation of the doubly reduced species to effectively catalyze hydrogen evolution.

Absorption↗
DOE OSTI2021

Installation of Lysimeters Near Engineered Trench 3

Three new lysimeter stations were installed on the north rim of Engineered Trench 3 (Figure ES-1)at the E-Area Low Level Waste Facility (ELLWF). These stations were designated as ET3-VL-3, ET3-VL-4, and ET3-VL-5. The stations were installed in a line with the two existing stations (ET3-VL-1 and ET3-VL-2) and spaced approximately 100 ft apart. Two lysimeters were installed at each station at the depths shown in Table ES-1. Lysimeter placement was based on borehole lithology and was comparable to the existing lysimeter stations. The deepest lysimeter at each of the three new lysimeter stations was designated as the action-level lysimeter.Following installation, the lysimeters were purged and placed under vacuum for sampling. Sampling occurred in December 2020 with each lysimeter producing 1000 ml of water for tritium analysis. All lysimeters were below the administrative limit for ET3 (43.7 pCi/ml) with all but ET3-VL-5 (222) at background levels. ET3-VL-5 (222) had a tritium concentration of 27.5 pCi/ml.With the addition of the new lysimeters at ET3, the vadose zone monitoring system is now comprised of 309 active lysimeters at 102 lysimeter stations. There are 93 action-level lysimeters at 102 stations

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

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
DOE OSTI2025

wa-hls4ml: A Benchmark and Surrogate Models for hls4ml Resource and Latency Estimation

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs. These advancements have solved major obstacles, but also exposed new challenges. For example, processes that were not previously considered bottlenecks, such as hardware synthesis, are becoming limiting factors in the rapid iteration of designs. To mitigate these emerging constraints, multipleefforts have been undertaken to develop an ML-based surrogate model that estimates resource usage of synthesized ML accelerator architectures. We introduce wa-hls4ml, a benchmark for ML accelerator resource and latency estimation, and its corresponding initial dataset of over 680 000 fully connected and convolutional neural networks, all synthesized using hls4ml and targeting Xilinx FPGAs. The benchmark evaluates the performance of resource and latency predictors against several common ML model architectures, primarily originating from scientific domains, as exemplar models, and the average performance across a subset of the dataset. Additionally, we introduce GNN- and transformer-based surrogate models that predict latency and resources for ML accelerators. We present the architecture and performance of the models and find that the models generally predict latency and resources for the 75% percentile within several percent of the synthesized resources on the synthetic test dataset.

Hawks, Benjamin G. [Fermilab]↗
DOE OSTI2023

FAIR AI models in high energy physics

Abstract The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research software and other digital products is an active area of research. Machine learning models—algorithms that have been trained on data without being explicitly programmed—and more generally, artificial intelligence (AI) models, are an important target for this because of the ever-increasing pace with which AI is transforming scientific domains, such as experimental high energy physics (HEP). In this paper, we propose a practical definition of FAIR principles for AI models in HEP and describe a template for the application of these principles. We demonstrate the template’s use with an example AI model applied to HEP, in which a graph neural network is used to identify Higgs bosons decaying to two bottom quarks. We report on the robustness of this FAIR AI model, its portability across hardware architectures and software frameworks, and its interpretability.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.

Anomaly detection↗
DOE OSTI2025

Reagent-free Hyperspectral Diagnosis of SARS-CoV-2 Infection in Saliva Samples

Rapid, reagent-free pathogen-agnostic diagnostics that can be performed at the point of need are vital for preparedness against future outbreaks. Yet, many current strategies are pathogen-specific and require several reagents. We present hyperspectral sensing, using light to non-invasively measure the composition of several molecules to form a spectral signature, to overcome these barriers. To generate these spectral signatures, we present the ProSpectral TM V1, a novel, miniaturized hyperspectral platform with high spectral resolution with two mini-spectrometers. Furthermore, we developed state-of-the-art ML pipelines for near real-time analysis of spectral signatures in saliva samples. We found that we could accurately identify SARS-CoV-2 infection status in double-blinded saliva samples and demonstrate 100% accuracy on a hold out test dataset. To our knowledge, this establishes the fastest hyperspectral diagnostic platform and in a small form factor, and executable with liquid samples, without ligands or reagents, all while maintaining PCR level specificity and sensitivity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .

36 MATERIALS SCIENCE↗
DOE OSTI2025

Harnessing Machine Learning for Agnostic Biodetection

The United States’ current list-based approach to biodefense is limited because it considers only known biological agents. Alternatively, developing and adopting a system based on agent-agnostic signatures would enable detection and characterization of both known and novel agents, thereby engendering greater adaptability in the face of an evolving threat landscape. Machine learning (ML) could aid in such a transition, as it can recognize and encode highly complex patterns from multiple input data modalities and has already demonstrated success in many healthcare and defense applications. Functionalizing ML for environmental biodetection requires understanding current technical capabilities. In this article, we provide a systematic review of existing ML platforms and discuss anticipated development efforts needed to achieve effective ML-enabled, agnostic biodetection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR

The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (ELM)-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta ($\beta_\textrm{N}$) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance $\beta_\textrm{N}$ efficiently. The integrated process triggers the n = 1 Edge-localized RMP (ERMP) at the L–H transition timing using the real-time Machine Learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same $\beta_\textrm{N}$ by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing $\beta_\textrm{N}$ during the suppression phase and sustaining the $\beta_\textrm{N}$-enhanced suppression state by optimizing the RMP strength. The integrated process achieves $\beta_\textrm{N}$ up to ~2.65 during the suppression phase, which is ~10% higher than the previous KSTAR record but ~6% lower than the target of the K-DEMO first phase ($\beta_\textrm{N}$ = 2.8), and maintains the suppression phase above the lower limit of target $\beta_\textrm{N}$ (= 2.4) for ~4 s (~60$\tau_\textrm{E}$). In addition to $\beta_\textrm{N}$ enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of $\beta_\textrm{N}$ compared to the adaptive control with the n = 1 Conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of $\beta_\textrm{N}$ and the electron temperature.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Results and lessons learned from accelerating radio frequency modeling using machine learning [slides]

The “advanced tokamak” reactor concept is a leading candidate for a steady state fusion pilot plant. An advanced tokamak (AT) sustains a majority of the required plasma current with effects resulting from maintenance of the peaked pressure at the device center. This current is augmented by auxiliary current drive sources. These auxiliary actuators may consist of neutral particle beams and/or radio frequency (RF) systems such as lower hybrid current drive (LHCD) and high harmonic fast wave (HHFW) current drive using radio and microwaves from antennas. The primary focus of this work is to develop models of RF current profile control suitable for use in integrated modeling frameworks and for real-time control in experiments. Direct physics models of RF current drive can be computationally intensive. In order to achieve predictive times appropriate for the thousands of calls needed in real-time control of experiments and for use in integrated models, we will apply modern machine learning (ML) techniques to accelerate these models and interpolate their results. To generate the fast and accurate models for use in control level algorithms and integrated modeling we need to replace present models with high dimensional interpolation of their results. We will perform additional simulations across a broader parameter range for EAST and other tokamaks in different physics regimes (Alcator C-Mod, DIII-D, WEST, CFETR, ARC, ITER) and combine them into a larger database for training and testing of the ML models. Further testing of the control level models with experimental current profile data from EAST and C-Mod tokamaks will provide additional confirmation of the control level model before integration in a tokamak control system or integrated modeling suite. ML will be used to optimize the selection of training data consisting of RF current driven at different values of density profile, temperature profile, plasma current, and wavenumber. ML will also be used to facilitate classification of current drive from these input data. The output of this effort will be a validated classifier capable of determining the current drive profiles for HHFW CD and LHCD on a mille-second timescale. This will provide a breakthrough capability enabling real-time control of RF driven current profiles in experiments including ITER ICRF and use integrated modeling frameworks requiring thousands of current profile calculations in discharge simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Gas-Phase Complexes of Americium and Lanthanides with a Bis-triazinyl Pyridine: Reactivity and Bonding of Archetypes for F-Element Separations

Bis-triazinyl pyridines (BTPs) exhibit solution selectivity for trivalent americium over lanthanides (Ln), the origins of which remain uncertain. Here in this paper, electrospray ionization was used to generate gas-phase complexes [ML 3 ] 3+ , where M = La, Lu, or Am and L is EtBTP 2,6-bis(5,6-diethyl-1,2,4-triazin-3-yl)-pyridine. Collision-induced dissociation (CID) of [ML 3 ] 3+ in the presence of H 2 O yielded a protonated ligand [L(H)] + and hydroxide [ML 2 (OH)] 2+ or hydrate [ML(L–H)(H 2 O)] 2+ , where (L–H) - is a deprotonated ligand. Although solution affinities indicate stronger binding of BTPs toward Am 3+ versus Ln 3+ , the observed CID process is contrastingly more facile for M = Am versus Ln. To understand the disparity, density functional theory was employed to compute potential energy surfaces for two possible CID processes, for M = La and Am. In accordance with the CID results, both the rate determining transition state barrier and the net energy are lower for [AmL 3 ] 3+ versus [LaL 3 ] 3+ and for both product isomers, [ML 2 (OH)] 2+ and [ML(L–H)(H 2 O)] 2+ . More facile removal of a ligand from [AmL 3 ] 3+ by CID does not necessarily contradict stronger Am 3+ –L binding, as inferred from solution behavior. In particular, the formation of new bonds in the products can distort kinetics and thermodynamics expected for simple bond cleavage reactions. In addition to correctly predicting the seemingly anomalous CID behavior, the computational results indicate greater participation of Am 5f versus La 4f orbitals in metal–ligand bonding.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Online LIBS–ML Framework for Dynamic Characterization of Heterogeneous Waste-Derived Gasification Feedstocks

LIBS−ML framework for real time feedstock characterization during continuous conveyor transport Heterogeneous waste derived feedstocks (e.g., waste coal, biomass and blends) introduce rapid variability in heating value and ash chemistry that affect gasifier operation, yet conventional laboratory characterization techniques are too slow to support proactive control. To address this gap, this study reports on an online, in situ, dynamic characterization framework that couple’s laser-induced breakdown spectroscopy (LIBS) with leakage safe machine learning (ML) regression to deliver real time, decision quality predictions of gasifier relevant properties. A controlled sample matrix spanning two different waste coals, two different biomasses, and engineered blends under two particle size conditions were constructed and benchmarked using standardized laboratory analyses for proximate/ultimate properties and ash composition. LIBS spectra were acquired dynamically as material flowed on a conveyor belt, using high energy 1064 nm laser ablation and shot averaging to improve repeatability and precision. Supervised regression models (multi layer perceptron (MLP) /artificial neural network (ANN), random forest (RF), and support vector regression (SVR)) and an optimized weighted ensemble were trained on emission line feature sets using nested cross validation with Bayesian hyperparameter tuning and validated against an independent hold out set. The proposed LIBS−ML workflow achieves near laboratory predictive fidelity across parametric targets (including higher heating value (HHV), ash content, fixed carbon, sulfur, major ash forming oxides, and initial deformation temperature (IDT)), with the weighted ensemble providing a robust default predictor under dynamic measurement conditions. These results demonstrate a practical pathway for real time feedstock characterization that can enable feedforward adjustments and more resilient gasifier operation for variable quality waste derived fuels.

Biomass↗
DOE OSTI2026

Hierarchical Testing of a Hybrid Machine Learning‐Physics Global Atmosphere Model

Machine learning (ML)-based models have demonstrated high skill and computational efficiency, often outperforming conventional physics-based models in weather and subseasonal predictions. While prior studies have assessed their fidelity in capturing synoptic-scale atmospheric dynamics, their performance across timescales and under out-of-distribution forcing, such as +3K or +4K uniform-warming forcings, and the sources of biases remain elusive, to establish the model's reliability for Earth science. Here, we design three sets of experiments targeting synoptic-scale phenomena, interannual variability, and out-of-distribution uniform-warming forcings. We evaluate the Neural General Circulation Model (NeuralGCM), a hybrid model integrating a dynamical core with ML-based component, against observations and physics-based Earth system models (ESMs). At the synoptic scale, NeuralGCM captures the evolution and propagation of extratropical cyclones with performance comparable to ESMs. At the interannual scale, when forced by El Niño-Southern Oscillation sea surface temperature (SST) anomalies, NeuralGCM successfully reproduces associated teleconnection patterns but exhibits deficiencies in capturing nonlinear response. Under out-of-distribution uniform-warming forcings, NeuralGCM simulates similar responses in global-average temperature and precipitation and reproduces large-scale tropospheric circulation features similar to those in ESMs. Notable weaknesses include overestimating the tracks and spatial extent of extratropical cyclones, biases in the teleconnected wave train triggered by tropical SST anomalies, and differences in upper-level warming and stratospheric circulation responses to SST warming compared to physics-based ESMs. The causes of these weaknesses were explored. Despite the noted weaknesses, NeuralGCM reproduces responses across experiments reasonably and performs comparably to ESMs. By integrating a dynamical core with ML, NeuralGCM shows potential for developing ML-based ESMs.

global warming↗
DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

42 ENGINEERING↗