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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

RNA Splicing Events in Circulation Distinguish Individuals With and Without New-onset Type 1 Diabetes

Context: Alterations in RNA splicing may influence protein isoform diversity that contributes to or reflects the pathophysiology of certain diseases. Whereas specific RNA splicing events in pancreatic islets have been investigated in models of inflammation in vitro, how RNA splicing in the circulation correlates with or is reflective of type 1 diabetes (T1D) disease pathophysiology in humans remains unexplored. Objective: To use machine learning to investigate if alternative RNA splicing events differ between individuals with and without new-onset T1D and to determine if these splicing events provide insight into T1D pathophysiology. Methods: RNA deep sequencing was performed on whole blood samples from 2 independent cohorts: a training cohort consisting of 12 individuals with new-onset T1D and 12 age- and sex-matched nondiabetic controls and a validation cohort of the same size and demographics. Machine learning analysis was used to identify specific isoforms that could distinguish individuals with T1D from controls. Results: Distinct patterns of RNA splicing differentiated participants with T1D from unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects. Conclusion: Alternative RNA splicing events in whole blood are significantly enriched in individuals with new-onset T1D and can effectively distinguish these individuals from unaffected controls. Further, our findings also suggest that RNA splicing profiles offer the potential to provide insights into disease pathogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

The influence of exposure to early-life adversity on agency-modulated reinforcement learning

Agency beliefs influence how humans learn from different contexts and outcomes. Research demonstrates that stressors, such as exposure to early-life adversity (ELA), are associated with both agency beliefs and learning, but how these processes interact remains unclear. The current study investigated whether exposure to ELA influences agency and interacts with reinforcement learning in adults. Replicating prior behavioral and computational work, ELA resulted in decreased learning, while increased adversity severity was associated with decreased latent agency beliefs. These findings suggest that exposure to adversity in childhood has a nuanced impact on reinforcement learning and agency beliefs in adulthood.

Neurosciences & Neurology↗
DOE OSTI2024

Evolution of connectivity architecture in the Drosophila mushroom body

Brain evolution has primarily been studied at the macroscopic level by comparing the relative size of homologous brain centers between species. How neuronal circuits change at the cellular level over evolutionary time remains largely unanswered. Here, using a phylogenetically informed framework, we compare the olfactory circuits of three closely related Drosophila species that differ in their chemical ecology: the generalists Drosophila melanogaster and Drosophila simulans and Drosophila sechellia that specializes on ripe noni fruit. We examine a central part of the olfactory circuit that, to our knowledge, has not been investigated in these species—the connections between projection neurons and the Kenyon cells of the mushroom body—and identify species-specific connectivity patterns. We found that neurons encoding food odors connect more frequently with Kenyon cells, giving rise to species-specific biases in connectivity. These species-specific connectivity differences reflect two distinct neuronal phenotypes: in the number of projection neurons or in the number of presynaptic boutons formed by individual projection neurons. Finally, behavioral analyses suggest that such increased connectivity enhances learning performance in an associative task. Our study shows how fine-grained aspects of connectivity architecture in an associative brain center can change during evolution to reflect the chemical ecology of a species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Resolving Mixtures of Soot Characterized by SP-AMS Spectra Using a Latent Dirichlet Allocation Model

Soot produced by detonation or combustion events exhibits different chemical properties depending on the fuel, device construction, and environmental conditions in which the event occurs. These properties can be useful for defining relevant signatures for probabilistically identifying the different types of events that occurred, based on the soot that is produced from these events. However, it is rare to observe samples of soot from a detonation or combustion that are not contaminated by outside particles. In this paper, we present a method for resolving mixtures of soot to determine the contributions of sources that may be present in samples of recovered soot. We use Latent Dirichlet Allocation to describe the generative process for a sample of recovered soot, and use Variational Bayesian Inference to learn about the parameters associated with the generative model. We demonstrate the utility of this method by considering real samples of mixtures of soot under various frameworks to show that the model is able to identify the different components present in a sample of soot as well as their mixing proportions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Smart CO2 Transport-Route Planning Tool: Providing Data and Insights for Accelerating Carbon Transport & Storage Deployment

Overview presentation given at the 2024 FECM / NETL Carbon Management Research Project Review Meeting on NETL's Bipartisan Infrastructure Law-funded Smart CO2 Transport-Route Planning Tool and associated geodatabase. This machine learning informed, data-driven public resource was designed to inform regulators, industry, and researchers plan and develop safe and efficient transport routes across the country.

Romeo, Lucy↗
DOE OSTI2025

Immersive Digital Twin Laboratory for Engineering Education (CRADA Final Report)

This project aimed to create an immersive digital twin laboratory that incorporates advanced tracking and visualization capabilities. In collaboration with Fort Lewis College, NREL designed a state-of-the-art physical visualization laboratory, developed a software platform to enable interaction with tracked physical objects in the laboratory, and provided proof-of-concept curricula that included manipulating these tracked objects. The project was initiated to address the growing need for innovative educational tools in engineering education. As renewable energy systems, particularly solar installations, become more complex, there is a pressing need to bridge the gap between theoretical knowledge and practical application. Traditional methods of teaching solar engineering concepts often fall short of providing students with a comprehensive, hands-on understanding. This immersive digital twin laboratory was conceived to fill that gap by creating a safe, non-energized setting where students can interact with augmented solar installation objects, gaining valuable insights into system performance, design, and maintenance. The project utilized extended reality (XR) technologies, including head-mounted displays (HMDs) and a whole-room optical motion tracking system, to connect physical objects with their digital twins in real time. The laboratory was equipped with MagicLeap 2 HMDs, supported by a Vicon Vero 2.2 Optical Tracking System, which provided precise 6-degrees-of-freedom (6-DOF) tracking. We developed a software platform to manage the interaction between the tracked physical objects and their virtual counterparts, enabling real-time data synchronization, object recognition, and virtual overlays. We designed the system to be flexible and extendable, allowing for future integration of additional objects and curriculum. This research advances the field of engineering education by demonstrating the potential of immersive digital twin environments. The laboratory provides a dynamic learning space where students can experiment, collaborate, and learn without the risks associated with live experimentation. The ability to simulate and manipulate solar installation objects under various conditions has broad implications for workforce development, particularly in renewable energy. The project also highlights the economic feasibility of using XR technologies in educational settings, offering a cost-effective solution for institutions looking to enhance their curriculum. By fostering a deeper understanding of solar energy systems, this work contributes to the broader goal of supporting the global energy transition and preparing the next generation of engineers and technicians.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Machine Learning Aided Modeling of Granular Materials: A Review

Artificial intelligence (AI) has become a buzzy word since Google’s AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader category of AI has obtained considerable attention in the research community of granular materials. This work offers a detailed review of the recent advances in machine learning-aided studies of granular materials from the particle-particle interaction at the grain level to the macroscopic simulations of granular flow. This work will start with the application of machine learning in the microscopic particle-particle interaction and associated contact models. Then, different neural networks for learning the constitutive behaviour of granular materials will be reviewed and compared. Finally, the macroscopic simulations of practical engineering or boundary value problems based on the combination of neural networks and numerical methods are discussed. We hope readers will have a clear idea of the development of machine learning-aided modelling of granular materials via this comprehensive review work.

42 ENGINEERING↗
DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

MLOps for Beam Controls

Machine learning operations (MLOps) is the standardization and streamlining of the ML development lifecycle to address the challenges associated with large-scale machine learning applications. The full MLOps pipeline consists of open-source tools: DataHub, MinIO and MLflow. It is being used for dataset management and model development to handle changing data dependencies, varying business needs, reproducibility, and diverse teams working with differing tools and skills. To demonstrate the completion of an MLOps pipeline for particle accelerator operations, we are deploying a simple script that computes settings for the Booster’s gradient magnet power supply. Once the demonstration is complete, we will develop and deploy ML-based optimization algorithms to improve Booster’s overall efficiency. This MLOps pipeline opens the gate to systematically develop and deploy ML applications for accelerator controls and diagnostics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Constitutive and inducible oleoresin defenses share genetic architectures and mechanisms in Pinus taeda

The oleoresin defense system of loblolly pine (Pinus taeda) protects trees from insects and pathogens and is an important source of renewable biofuels and chemicals, but the genetic basis of oleoresin production is poorly understood. We characterized the genetic architecture of oleoresin flow, resin canal number, stem wood terpene content, and monoterpene composition in two clonal populations of P. taeda. We used quantitative genetic analyses, genome-wide association studies (GWASs), multiplex network learning, and gene expression profiling to elucidate shared gene networks underlying defense traits and to identify high-quality candidates for breeding and engineering loblolly pine. Genetic analyses revealed polygenic inheritance and trait-to-trait correlations provide strong evidence for shared genes regulating constitutive and induced oleoresin flow. We identified 236 single nucleotide polymorphisms associated with oleoresin flow, resin canal number, and terpene composition and highlight candidate genes likely involved in terpene biosynthesis, cambial meristem reprogramming, and pathogen perception and immune signaling. Fourteen GWAS candidates were methyl jasmonate-responsive in tissues where resin canals initiate and terpene production occurs. Integrating quantitative genetics, GWAS, gene expression, and multiplex network analyses enabled the prioritization of high-quality candidate genes. This work advances the development of more resilient loblolly pine optimized for ecological performance, renewable chemical, and biofuel production.

genome-wide association study↗
DOE OSTI2024

Task-specific sensor optical designs

A method and system architecture for designing a compressive sensing matrix for machine learning includes receiving an image associated with a classification task and; generating a sensing matrix. The sensing matrix includes an array of nonzero elements of the image. A prism array of prism elements is in communication with the sensing matrix. A row of values corresponding with an input angle of the prism array is mapped to a respective column corresponding with a detector. Then the detector detects light refracted at an output angle dictated by the physical shape of the prism element. A physical model of the detector is fabricated and generates a compressed representation of the image. A machine learning classification algorithm is applied to the compressed representation of the image and generates an optimized non-invertible final determination of the image.

Birch, Gabriel Carlisle↗
DOE OSTI2026

Neural entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws

We propose a neural entropy-stable conservative flux form neural network (NESCFN) for learning hyperbolic conservation laws and their associated entropy functions directly from solution trajectories, without requiring any predefined numerical discretization. While recent neural network architectures have successfully integrated classical numerical principles into learned models, most rely on prior knowledge of the governing equations or assume a fixed discretization. Our approach removes this dependency by embedding entropy-stable design principles into the learning process itself, enabling the discovery of physically consistent dynamics in a fully data-driven setting. By jointly learning both the flux function and a corresponding entropy, NESCFN promotes conservation and entropy dissipation, which is critical for long-term stability and fidelity in the system of hyperbolic conservation laws. Furthermore, numerical results demonstrate that the method achieves stability and conservation over extended time horizons and accurately captures shock propagation speeds, even without oracle access to future-time solution profiles in the training data.

Conservative flux form↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
DOE OSTI2025

Microbial vitamin biosynthesis links gut microbiota dynamics to chemotherapy toxicity

ABSTRACT Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited. Here, we investigate capecitabine (CAP)-microbiome interactions through longitudinal metagenomic sequencing of stool from 56 advanced colorectal cancer patients. CAP significantly altered the gut microbiome, enriching for menaquinol (vitamin K2) biosynthesis genes. Transposon library screens, targeted gene deletions, and media supplementation revealed that menaquinol biosynthesis protectsEscherichia colifrom drug toxicity. Stool menaquinol gene and metabolite levels were associated with decreased peripheral sensory neuropathy. Machine learning models trained in this cohort predicted toxicities in an independent cohort. Taken together, these results suggest treatment-associated increases in microbial vitamin biosynthesis serve a chemoprotective role for bacterial and host cells. Further, our findings provide a foundation for in-depth mechanistic dissection, human intervention studies, and extension to other cancer treatments. IMPORTANCE Side effects are common during the treatment of cancer. The trillions of microbes found within the human gut are sensitive to anticancer drugs, but the effects of treatment-induced shifts in gut microbes for side effects remain poorly understood. We profiled gut microbes in colorectal cancer patients treated with capecitabine and carefully monitored side effects. We observed a marked expansion in genes for producing vitamin K2 (menaquinone). Vitamin K2 rescued gut bacterial growth and was associated with decreased side effects in patients. We then used information about gut microbes to develop a predictive model of drug toxicity that was validated in an independent cohort. These results suggest that treatment-associated increases in bacterial vitamin production protect both bacteria and host cells from drug toxicity, providing new opportunities for intervention and motivating the need to better understand how dietary intake and bacterial production of micronutrients like vitamin K2 influence cancer treatment outcomes.

Microbiology↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts

The recent integration of machine learning into materials design has revolutionized the understanding of structure–property relationships and optimization of material properties beyond the trial-and-error paradigm. On one hand, machine learning has significantly accelerated the development of atomically dispersed metal-nitrogen-carbon (M-N-C) electrocatalysts, which traditionally heavily relied on heuristic approaches. On the other hand, the primary challenge of leveraging machine learning to expedite M-N-C materials discovery lies in the cost associated with data collection. Here, we review recent machine learning integration strategies for M-N-C catalyst development, including discussions on the typical algorithms such as symbolic regression and convolutional neural networks employed for the theoretical design, synthesis optimization via active learning, and advanced microscopy characterization. Subsequently, we provide our perspective on potential near-future directions for furthering machine learning-assisted development of new M-N-C catalysts and elucidating the complex physicochemical mechanisms governing the selectivity, activity, and durability in this class of materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Decode the Workload: Training Deep Learning Models for Efficient Compute Cluster Representation

Monitoring the status of a high throughput computing cluster running computationally intensive production jobs is a crucial yet challenging system administration task due to the complexity of such systems. To this end, we train autoencoders using the Linux kernel CPU metrics of the cluster. Additionally, we explore assisting these models with graph neural networks to share information across threads within a compute node. The models are compared in terms of their ability to: 1) Produce a compressed latent representation that captures the salient features of the input, 2) Detect anomalous activity, and 3) Make distinction between different kinds of jobs run at Jefferson Lab. The goal is to have a robust encoder whose compressed embeddings are used for several downstream tasks. We extend this study further by deploying these models in a human-in-the-loop production-based setting for the anomaly detection task and discuss the associated implementation aspects such as continual learning and the criterion to generate alarms. This study represents a first step in the endeavor towards building self-supervised large-scale foundation models for computing centers.

Mohammed, Ahmed↗