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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Graph-based design of irregular metamaterials

In the field of metamaterial research, random structures offer a novel and less conventional approach compared to traditional periodic designs. Designing random metamaterials is challenging when it comes to ensuring intercon- nectivity, which is essential for manufacturability. This study introduces an innovative framework for generating random metamaterials using graph al- gorithms, ensuring connectivity and adaptability across various base shapes, including cylinders, triangles, pyramids, and cubes. By employing graph algorithms, our framework enhances the intuitiveness and efficiency of de- sign representation and manipulation, streamlining the design process. The framework generates families of designs that exhibit a wide range of prop- erty magnitudes that can be adjusted intuitively by modifying the input parameters. The rapid design process allows many designs to be generated, offering the user a multitude of solutions around the target property range. The designs can be effectively implemented in various fields and subjected to diverse analytical studies, including static, dynamic, and eigenfrequency assessments. We illustrate computational results for two key properties (stiff- ness and acoustic impedance), showcasing the method’s effectiveness through examples ranging from rod-based to cube-based designs. Here, the framework not only advances metamaterial research but also creates new opportunities for innovation in fields requiring customized material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Explainable Graph Learning for Particle Accelerator Operations

Particle accelerators are vital tools in physics, medicine, and industry, requiring precise tuning to ensure optimal beam performance. However, real-world deviations from idealized simulations make beam tuning a time-consuming and error-prone process. In this work, we propose an explanation-driven framework for providing actionable insight into beamline operations, with a focus on the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF). We represent beamline configurations as heterogeneous graphs, where setting nodes represent elements that human operators can actively adjust during beam tuning, and reading nodes passively provide diagnostic feedback. To identify the most influential setting nodes responsible for differences between any two beamline configurations, our approach first predicts the resulting changes in reading nodes caused by variations in settings, and then learns importance scores that capture the joint influence of multiple setting nodes. Experimental results on real-world CEBAF injector data demonstrate the framework’s ability to generate interpretable insights that can assist human operators in beamline tuning and reduce operational overhead.

Wang, Song [Univ. of Virginia, Charlottesville, VA↗
DOE OSTI2025

Search for Stable and Low-Energy Ce–Co–Cu Ternary Compounds Using Machine Learning

Cerium-based intermetallics have garnered significant research attention as potential new permanent magnets. In this study, we explore the compositional and structural landscape of Ce−Co−Cu ternary compounds using a machine learning (ML)- guided framework integrated with first-principles calculations. We employ a crystal graph convolutional neural network (CGCNN), which enables efficient screening for promising candidates, significantly accelerating the material discovery process. With this approach, we predict five stable compounds, Ce 3 Co 3 Cu, CeCoCu 2 , Ce 12 Co 7 Cu, Ce 11 Co 9 Cu, and Ce 10 Co 11 Cu 4 , with formation energies below the convex hull, along with hundreds of low-energy (possibly metastable) Ce−Co−Cu ternary compounds. Firstprinciples calculations reveal that several structures are both energetically and dynamically stable. Notably, two Co-rich low-energy compounds, Ce 4 Co 33 Cu and Ce 4 Co 31 Cu 3 , are predicted to have high magnetizations.

Chemical structure↗
DOE OSTI2018

Improving Unstructured Mesh Partitions for Multiple Criteria Using Mesh Adjacencies

The scalability of unstructured mesh based applications depends on partitioning methods that quickly balance the computational work while reducing communication costs. Zhou et al. [SIAM J. Sci. Comput., 32 (2010), pp. 3201{3227; J. Supercomput., 59 (2012), pp. 1218{1228] demonstrated the combination of (hyper)graph methods with vertex and element partition improvement for PHASTA CFD scaling to hundreds of thousands of processes. Our work generalizes partition improvement to support balancing combinations of all the mesh entity dimensions (vertices, edges, faces, regions) in partitions with imbalances exceeding 70%. Improvement results are then presented for multiple entity dimensions on up to one million processes on meshes with over 12 billion tetrahedral elements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Operational Intelligence in the ATLAS Continuous Integration System

Describes the role of the ATLAS Continuous Integration (CI) System in the ATLAS offline software development infrastructure • Outlines the CI system components and processes • Details Operational Intelligence techniques to accelerate CI jobs and lower operating costs • Explains the Directed Acyclic Graph (DAG) approach in CI pipelines • Reports achieved improvements

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Quantum many-body effects in x-ray spectra efficiently computed using a basic graph algorithm

The mounting interest in using x-ray spectroscopy for refined materials characterization calls for an accurate electronic-structure theory to interpret the x-ray near-edge fine structure. Here, we propose an efficient and unified framework to describe all the many-electron processes in a Fermi liquid after a sudden perturbation (such as a core hole). This problem has been visited by the Mahan-Noziéres-De Dominicis (MND) theory, but it is intractable to implement various Feynman diagrams within first-principles calculations. In our report, we adopt a nondiagrammatic approach and treat all the many-electron processes in the MND theory on an equal footing. Starting from a recently introduced determinant formalism [Phys. Rev. Lett. 118, 096402 (2017)], we exploit the linear dependence of determinants describing different final states involved in the spectral calculations. An elementary graph algorithm, breadth-first search, can be used to quickly identify the important determinants for shaping the spectrum, which avoids the need to evaluate a great number of vanishingly small terms. This search algorithm is performed over the tree-structure of the many-body expansion, which mimics a path-finding process. We demonstrate that the determinantal approach is computationally inexpensive even for obtaining x-ray spectra of extended systems. Using Kohn-Sham orbitals from two self-consistent fields (ground and core-excited state) as input for constructing the determinants, the calculated x-ray spectra for a number of transition metal oxides are in good agreement with experiments. Many-electron aspects beyond the Bethe-Salpeter equation, as captured by this approach, are also discussed, such as shakeup excitations and many-body wave function overlap considered in Anderson's orthogonality catastrophe.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Data-Driven Template Discovery Using Graph Convolutional Neural Networks

Modeling adversarial activities is a critical component of developing high-con?dence indicators of efforts to acquire, fabricate, proliferate, and/or deploy weapons of mass terror (WMTs). Current approaches to generating representative patterns of interest (a.k.a templates) from the real-world domains involve a Subject Matter Expert (SME)-guided manual process. The goal of Data-Driven Template Discovery (DDTD) is to use a (potentially small) set of SME generated templates to discover other previously unknown and interesting templates in an attributed graph. A template is an activity pattern describing a set of interactions among a group of nodes in the graph. The motivation behind DDTD is to expand the original set of templates, without having SMEs craft all the templates by hand. DDTD also provides seed templates to SMEs, to help them construct larger, high-?delity, and scenario-oriented templates. In these cases, obtaining a larger set of templates that are related (contain similar signals) to the original set is of great value. In this work, we propose to use Graph Convolutional Neural Networks (GCNs) to discover new templates that are heavily related to the original set. GCNs are a family of Neural Network (NN) architectures especially designed to work directly on graphs. In contrast to the traditional NNs, that require considerable amounts of labeled data, GCNs do not require a big labeled training set because they can directly leverage the graph structure instead. This property makes GCNs the perfect tool for creating activity templates.

Joaristi, Mikel↗
DOE OSTI2022

GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it remains notoriously challenging to inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because real-world graphs can be extremely large and sparse. Furthermore, the node degree of GCNs tends to follow the power-law distribution and therefore have highly irregular adjacency matrices, resulting in prohibitive inefficiencies in both data processing and movement and thus substantially limiting the achievable GCN acceleration efficiency. To this end, this paper proposes the first GCN algorithm and accelerator Co-Design framework dubbed GCoD which can largely alleviate the aforementioned GCN irregularity and boost GCNs' inference efficiency. Specifically, on the algorithm level, GCoD integrates a divide and conquer GCN training strategy that polarizes the graphs to be either denser or sparser in local neighborhoods without compromising the model accuracy, resulting in graph adjacency matrices that (mostly) have merely two levels of workload and enjoys largely enhanced regularity and thus ease of acceleration. On the hardware level, we further develop a dedicated two-pronged accelerator with a separated engine to process each of the aforementioned workloads, further boosting the overall utilization and acceleration efficiency. Extensive experiments and ablation studies validate that our GCoD consistently outperforms state-of-the-art designs in terms of accelerator efficiency while maintaining or even improving the task accuracy. Additionally, we visualize GCoD trained graph adjacency matrices to better understand its advantages. All codes and pre-trained models will be released upon acceptance.

You, Haoran↗
DOE OSTI2018

Weighted Distances in Scale-Free Configuration Models

In this paper we study first-passage percolation in the configuration model with empirical degree distribution that follows a power-law with exponent τ∈(2,3). We assign independent and identically distributed (i.i.d.) weights to the edges of the graph. We investigate the weighted distance (the length of the shortest weighted path) between two uniformly chosen vertices, called typical distances. When the underlying age-dependent branching process approximating the local neighborhoods of vertices is found to produce infinitely many individuals in finite time—called explosive branching process—Baroni, Hofstad and the second author showed in Baroni et al. (J Appl Probab 54(1):146–164, 2017) that typical distances converge in distribution to a bounded random variable. The order of magnitude of typical distances remained open for the τ∈(2,3) case when the underlying branching process is not explosive. We close this gap by determining the first order of magnitude of typical distances in this regime for arbitrary, not necessary continuous edge-weight distributions that produce a non-explosive age-dependent branching process with infinite mean power-law offspring distributions. This sequence tends to infinity with the amount of vertices, and, by choosing an appropriate weight distribution, can be tuned to be any growing function that is O(loglog n), where n is the number of vertices in the graph. We show that the result remains valid for the the erased configuration model as well, where we delete loops and any second and further edges between two vertices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Oak Ridge National Laboratory Technical Input for the Nuclear Regulatory Commission Review of the 2017 Edition of ASME Section III, Division 5, ‘High Temperature Reactors’

To assist the Nuclear Regulatory Commission in its decision making on endorsement of the American Society for Mechanical Engineers Boiler and Pressure Vessel Code Section III, Division 5 (2017 Edition) for development of advanced non-light water reactors, the following Division 5 portions were reviewed: Article HBB-2000 Material; Article HCB-2000 Material; Article HGB-2000 Material; Mandatory Appendix HBB-I-14 Tables and Figures; and, Nonmandatory Appendix HBB-U Guidelines for Restricted Material Specifications to Improve Performance in Certain Service Applications. In addition to the 2017 Edition, the same parts of the 2019 Edition have also been reviewed as indicated in various sections of the report. This review was conducted by a collaboration of national laboratory and private sector participants with significant industrial experience, including some heavy lifting and deep diving from Clarus Consulting, LLC., all intended to achieve an objective, independent, and practical perspective. The report provides recommendations, descriptions of the evaluation methods, and the source references for the data used. To build confidence required for endorsement of the Code, this review was conducted as a verification and validation of the above Code contents. The objective of verification is to ensure that the Code is free of error – direct or implied; contains the information needed for its use, including proper coverage of the Code-specified materials for the intended application, and completeness and adequacy of references to other portions of the Code. The objective of validation is to authenticate that the Code tabulations and graphs represent design inputs consistent with what are determined using rules and methods specified by the Code. The authentication process used data that were assembled and/or generated independent of Code development, while the methods of analysis followed Code-specified methods where appropriate. The designated portions for this review cover the five alloys codified for high temperature reactor applications in Division 5, i.e. 316 SS, 304 SS, 800H, 2¼Cr-1Mo, and 9Cr-1Mo-V, regarding their general requirements, permitted specifications and design stress intensity values for pressure-retaining applications, deterioration in service, fatigue acceptance test, permissible weld materials, tensile and yield strength, expected minimum stress-to-rupture values (including for Alloy 718), weld stress rupture factors, permissible materials for bolting use, and restricted specifications in certain service applications. Additionally, stress intensity values for bolting materials including 316 SS, 304 SS and alloy 718 were reviewed. Analysis and discussion are also provided on contents outside of these designated Code portions where it was deemed relevant and necessary to develop a technically sound understanding of issues relating to the designated portions. Due to unavailability of sufficient test data on welds during the review period, the weld stress rupture factors in Tables HBB-I-10.14A to E, which cover a total of ten tables for the five alloys welded with twenty-eight different weld metals (some with similar properties), have been deferred to a future review effort. The review identified mainly two types of issues. The first type includes instances where the Code is found factually incomplete or incorrect, such as obsolete materials specifications listings, missing tabulation of stresses for bolting. Changes to the Code are recommended in these cases. The second type of issue includes instances where the Code tabulations and graphs are found to be less conservative than the review analysis results. In these cases, recommendations are made for further review and consideration where the difference in conservatism exceeds 10%, which is our threshold for questioning technical adequacy, meriting a risk assessment by the Nuclear Regulatory Commission and/or reactor designers. It is noted that this effort has been executed using all available data and established methods of analysis, including methods and criteria specified and used by the Code. As such, the findings that are presented in quantitative detail, in a format for convenient comparison with the Code, and with identification of where further review is recommended, should provide a sound technical basis for decisions about quantifying the implications of the reduced design margins and technical adequacy/inadequacy to form a basis for conditioning specific Code tabulation values on endorsement. Recommendations for specific changes to the Code, however, entail design conservatism considerations beyond the scope of this review effort, and are not made in this report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Finding diverse ways to improve algebraic connectivity through multi-start optimization

The algebraic connectivity, also known as the Fiedler value, is a spectral measure of network connectivity that can be increased through edge addition. We present an algorithm for producing many diverse ways to add a fixed number of edges to a network to achieve a near optimal Fiedler value. Previous Fielder value optimization algorithms (i.e. the greedy algorithm) output only one solution. Obtaining a single solution is rarely good enough for real-world network redesign problems, as practical constraints (political, physical or financial) may prevent implementation. Our algorithm takes a multi-start optimization approach, adding a random initial edge and then applies a greedy heuristic to improve the Fiedler value. The random choice moves us to a new region of the search space, enabling discovery of diverse solutions. Additionally, we present a Determinantal Point Process framework for quantifying diversity. We then apply a Markov chain Monte Carlo technique to sift through the large number of output solutions and locate a smaller, more manageable collection of highly diverse solutions that can be presented to network redesign engineers. We demonstrate the effectiveness of our algorithm on real-world graphs with varied structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML-based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass-Conserving-Perceptron (MCP) as a way to bridge the gap between PC-based and ML-based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass-conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off-the-shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall-runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML-based physical-conceptual representation of the coupled nature of mass-energy-information flows through geoscientific systems.

58 GEOSCIENCES↗
DOE OSTI2021

Graph Convolutional Network-Based Topology Embedded Deep Reinforcement Learning for Voltage Stability Control

Topological variations in power system is a common phenomenon and can impose significant challenges to traditional controllers of power system. Recent study revealed the strength of deep reinforcement learning (DRL) based approaches in power system preventive and corrective control. But topological variations are difficult to capture using classical fully connected neural network (FCN) model and has not been explicitly modeled in previous work. Hence, we develop a Graph Convolutional Network (GCN) based DRL framework to tackle topology changes in control design of power system. The GCN model exploits the graph structure of the power network and helps the DRL agent to embed the topology information during learning process. Our GCN based approach is evaluated using the IEEE-39 bus system and it outperforms the FCN-based DRL scheme in terms of training convergence and control performance considering grid topology changes.

Hossain, Ramij Raja↗
DOE OSTI2024

Automating Log Synthesis and Visualization with Python and Splunk

The goal of this project is to automate log analysis by utilizing Splunk, Bash, and Python together. Simplifying the monitoring and analysis of network traffic was the main goal. In order to accomplish this, a Bash script was created to use 'tcpdump' to automate network sniffing. It also included a 24-hour file rotation mechanism to effectively manage the pcap files that were generated. After that, a Python script was written to read these pcap files and retrieve pertinent data about network traffic. After processing the collected data, Splunk is used to summarize the important metrics and visualize said information with relevant graphs.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

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

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2017

RuleBuilder

RuleBuilder is a tool for drawing graphs represented by the BioNetGen rule-based modeling language (BNGL), a domain-specific language used to formulate mathematical models of biochemical interaction networks. This framework provides an intuitive plain-text representation of such networks using a graph-based formalism. The interactions between the model's components are graph-rewriting rules that denote the minimum context needed for the interaction to occur. In some cases the context required for reactions to occur can grow quite large, and the lengthy textual description of the interaction can be difficult to interpret or debug. RuleBuilder is designed to make this process simpler through visualization of individual rule or pattern strings. The software assists in the reading of existing models by converting the BNGL strings of interest into a graph-based representation composed of nodes and edges. RuleBuilder also enables the user to build the visual representation directly from its interface, composing the BNGL string as components and operators are added to the graphical object.

Fricke, Matthew↗