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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 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↗
DOE OSTI2024

Machine Learning Accelerates Innovation in Perovskite Manufacturing Scale-up (Final Technical Report (FTR))

We propose to address the challenge of the vast parameter space associated with perovskite manufacturing optimization, by developing a machine learning (ML)-assisted optimization framework for a scalable perovskite PV manufacturing tool. This framework will be interpretable, sequential, and rapidly adaptable to upgraded systems (e.g., via transfer learning). The tool is an open-air rapid spray plasma process (RSPP) of perovskite films, which has already been established at Stanford and is a unique platform to test and deploy the proposed ML-guided framework because the RSPP technique is able to conduct optimization experiments with a high throughput, and easily adjust a wide range of process variables.

14 SOLAR ENERGY↗
DOE OSTI2024

A Gaussian process based surrogate approach for the optimization of cylindrical targets

Simulating direct-drive inertial confinement experiments presents significant computational challenges, both due to the complexity of the codes required for such simulations and the substantial computational expense associated with target design studies. Machine learning models, and in particular, surrogate models, offer a solution by replacing simulation results with a simplified approximation. In this study, we apply surrogate modeling and optimization techniques that are well established in the existing literature to one-dimensional simulation data of a new cylindrical target design containing deuterium–tritium fuel. These models predict yields without the need for expensive simulations. We find that Bayesian optimization with Gaussian process surrogates enhances sampling efficiency in low-dimensional design spaces but becomes less efficient as dimensionality increases. Nonetheless, optimization routines within two-dimensional and five-dimensional design spaces can identify designs that maximize yield, while also aligning with established physical intuition. Optimization routines, which ignore constraints on hydrodynamic instability growth, are shown to lead to unstable designs in 2D, resulting in yield loss. However, routines that utilize 1D simulations and impose constraints on the in-flight aspect ratio converge on novel cylindrical target designs that are stable against hydrodynamic instability growth in 2D and achieve high yield.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Invertible Temper Modeling using Normalizing Flows and the Effects of Structure Preserving Loss

Advanced manufacturing research and development is typically small-scale, owing to costly experiments associated with these novel processes. Deep learning techniques could help accelerate this development cycle but frequently struggle in small-data regimes like the advanced manufacturing space. While prior work has applied deep learning to modeling visually plausible advanced manufacturing microstructures, little work has been done on data-driven modeling of how microstructures are affected by heat treatment, or assessing the degree to which synthetic microstructures are able to support existing workflows. We propose to address this gap by using invertible neural networks (normalizing flows) to model the effects of heat treatment, e.g., tempering. The model is developed using scanning electron microscope imagery from samples produced using shear-assisted processing and extrusion (ShAPE) manufacturing. This approach not only produces visually and topologically plausible samples, but also captures information related to a sample’s material properties or experimental process parameters. We also demonstrate that topological data analysis, used in prior work to characterize microstructures, can also be used to stabilize model training, preserve structure, and improve downstream results. We assess directions for future work and identify our approach as an important step towards end-to-end deep learning system for accelerating advanced manufacturing research and development.

Howland, Sylvia↗
DOE OSTI2024

Chemical reaction enhanced graph learning for molecule representation

Abstract Motivation Molecular representation learning (MRL) models molecules with low-dimensional vectors to support biological and chemical applications. Current methods primarily rely on intrinsic molecular information to learn molecular representations, but they often overlook effectively integrating domain knowledge into MRL. Results In this article, we develop a reaction-enhanced graph learning (RXGL) framework for MRL, utilizing chemical reactions as domain knowledge. RXGL introduces dual graph learning modules to model molecule representation. One module employs graph convolutions on molecular graphs to capture molecule structures. The other module constructs a reaction-aware graph from chemical reactions and designs a novel graph attention network on this graph to integrate reaction-level relations into molecular modeling. To refine molecule representations, we design a reaction-based relation learning task, which considers the relations between the reactant and product sides in reactions. In addition, we introduce a cross-view contrastive task to strengthen the cooperative associations between molecular and reaction-aware graph learning. Experiment results show that our RXGL achieves strong performance in various downstream tasks, including product prediction, reaction classification, and molecular property prediction. Availability and implementation The code is publicly available at https://github.com/coder-ACAC/RLM.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗
DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
DOE OSTI2025

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting

Abstract Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improves the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to achieve desirable outcomes in the healthcare field.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Quantifying Capital Cost Reduction Pathways for Advanced Nuclear Reactors

The framework developed in this study is provided both as an excel sheet (https://inl.gov/content/uploads/2023/11/Nuclear-Reactor-Cost-Reduction-Pathway-Spreadsheet-Tool.xlsx) and a Python (Jupyter) Notebook (link: https://github.com/accert-dev/ACCERT/tree/main/Cost%20Reduction). Capital cost considerations are one of the primary inhibitors to the large-scale deployment of nuclear power plants. While it is widely accepted that first units will likely be expensive and relatively uncompetitive, it is reasonable to expect that subsequent units, built in relative quick succession, will be cheaper as they benefit from the so-called “learning effects”. However, the large degree of uncertainty associated with this parameter renders it challenging for first movers to invest in the first few expensive units. To resolve this impasse, the U.S. Department of Energy’s Advanced Nuclear Liftoff study advocated for the formation of large, committed order books of plants of the same technology to spread the costs across several units and kickstart the nuclear supply chain. The study also advocated best practices for avoiding overruns and keeping reactors on budget. This report builds on these key recommendations by attempting to quantify specific pathways toward cost reduction for nuclear energy. A capital cost estimation framework was built to untangle the effect of learning into a subset of key cost drivers, referred to as “levers”. Collectively, the choice of these levers is intended to reflect the decision-making of high-level stakeholders like plant owners and the government. In addition to the size of the firm orderbook, these levers included (a) cost drivers that are most often attributed to cost overruns such as architect/engineering (A/E) proficiency, construction proficiency, procurement service proficiency, design completion prior to the start of construction, and design maturity, and (b) cost reduction strategies such as modular construction, cross-site standardization, safety classification of the reactor building, and of the balance of plant. Two advanced reactor designs were leveraged as use cases and bottom-up cost estimates made with assumptions consistent with a well-executed first-of-a-kind project (WE-FOAK, i.e., almost no overruns) were used as baselines for the models. Cost correlations were surveyed from the literature to determine the impact of important variables on projected timelines and costs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Lessons Learned from MARVEL Initial Fabrication and Testing

The Microreactor Applications Research Validation and Evaluation (MARVEL) project is intended to be amongst the first U.S. advanced reactor demonstrations in four decades. By virtue of being sponsored directly by the U.S. Department of Energy, the reactor is intended to benefit the broader nuclear community by exercising design processes, safety reviews, and supply chains. This project is intent on publicly documenting key lessons learned along the way toward demonstration. Building on a 2024 report that focused on lessons learned from the design and project management phase, this 2025 edition focuses on findings from the MARVEL guard vessel fabrication and primary coolant apparatus test (PCAT) testing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗
DOE OSTI2025

Towards Automated Assessment of Vulnerability Exposures in Security Operations

Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.

Huff, Philip↗
DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Machine Learned Force Field Modeling of Metal Organic Frameworks for CO2 Direct Air Capture

Metal organic frameworks (MOFs) are a large class of porous materials and have garnered significant interest due to their large surface areas and their tunable physical and chemical properties. Numerous prior studies have been performed to screen large databases of this material class for promising DAC sorbent materials. These studies have often relied on classical model potentials. While density functional theory (DFT) calculations have been shown to be very accurate for modeling the interaction of CO2 with MOFs, such calculations are too computationally demanding for statistically significant adsorption predictions. To overcome this barrier, we developed methods for training models to achieve DFT-level accuracy for the forces and energies associated with MOF flexibility and CO2 adsorption using machine learned force fields (MLFFs). These methods were parametrized based on DFT calculations of CO2 in a flexible MOF and used to predict MOF structural properties as well as CO2 adsorption in several MOFs.

Findley, John↗
DOE OSTI2026

Reducing systematic bias in machine learning applications to J/ψ signal extraction in high-energy nuclear physics

Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗
DOE OSTI2025

Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification" Willard et al. (2025).

This data release provides all data and code used in the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025)" to model stream temperature, evaluate, and assess results. The associated manuscript explores the effect of different ensemble construction techniques across different common machine learning (ML) architectures for predictions in unmonitored basins. Modeling was done using long short-term memory (LSTM), gated recurrent unit (GRU), temporal convolution network (TCN), and extreme gradient boosting (XGBoost) models, and stream site coverage spans 1362 locations across the conterminous United States. The ensemble construction techniques investigated include ensemble by random weight initialization, differing hyperparameters, different random subsets of training data, different subselections of input features, different architectures, and Monte Carlo Dropout. The data is organized into these items items:Code repository and data for the paper " "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantifications" Willard et al. (2025).Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code:- data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repositoryData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2025streamensembles,author = {Jared Willard and Charuleka Varadharajan},title = {Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification"},year = {2024},doi = {10.15485/2527393},publisher = {ESS-DIVE Repository},url = {https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2527393}}MLA: Willard, Jared, et al. Dataset for "Machine Learning Ensembles Can Enhance Hydrologic Predictions and Uncertainty Quantification". 2025. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗