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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 OSTI2022

Differentially Private K -Means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. Here, in this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Canadian Oil Sands Extraction and Upgrading: A Synthesis of the Data on Energy Consumption, CO 2 Emissions, and Supply Costs

As Canadian crude bitumen production from oil sands has increased in recent decades, the nation’s oil and gas industry has become a significant contributor to national greenhouse gas emissions. Canada has developed carbon emission reduction targets to meet its Nationally Determined Contributions and Mid-Century Strategy goals. A detailed profile of energy consumption pathways in the oil sands industry is necessary to identify potential areas of improvement and to monitor progress toward meeting emissions reduction targets. Much of the existing literature for oil sands modeling provides input assumptions with different technological boundaries. For a set of oil sands extraction and upgrading technologies, this study first reviews the literature and then quantifies energy input requirements, CO 2 emissions, and operating costs for a set of consistent technological boundaries and energy units. Summary results refer to requirements and costs at the production facility, excluding transportation and blending costs. An energy system diagram of oil sands production that matches these boundaries is provided, which can be used by integrated assessment models, oil sands companies, and government ministries to evaluate the present and future energy consumption and emissions pathways of the oil sands industry.

02 PETROLEUM↗
DOE OSTI2023

Data for: "Inorganic synthesis-structure maps in zeolites with machine learning..."

Repository containing all the data necessary to reproduce the paper "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". This content includes: raw data for all results in the article; figures appearing in the main paper; and Jupyter notebooks and scripts containing the code for analyzing/reproducing the results.

Schwalbe Koda, Daniel↗
DOE OSTI2025

cnor_pub: R code and data for nitrous oxide synthesis by purified bacterial cNOR

This zipped archive of a GitHub repository includes the experimental data (csv files) collected for the reduction of NO to N2O by purified Paracoccus denitrificans cytochrome c nitric oxide reductase (cNOR) and the R code (qmd files) used to analyze these data. A link to the corresponding GitHub repository is also provided.

Hegg, Eric L. [GLBRC - Michigan State University]↗
DOE OSTI2022

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

58 GEOSCIENCES↗
DOE OSTI2022

Text-mined dataset of gold nanoparticle synthesis procedures, morphologies, and size entities

Abstract Gold nanoparticles are highly desired for a range of technological applications due to their tunable properties, which are dictated by the size and shape of the constituent particles. Many heuristic methods for controlling the morphological characteristics of gold nanoparticles are well known. However, the underlying mechanisms controlling their size and shape remain poorly understood, partly due to the immense range of possible combinations of synthesis parameters. Data-driven methods can offer insight to help guide understanding of these underlying mechanisms, so long as sufficient synthesis data are available. To facilitate data mining in this direction, we have constructed and made publicly available a dataset of codified gold nanoparticle synthesis protocols and outcomes extracted directly from the nanoparticle materials science literature using natural language processing and text-mining techniques. This dataset contains 5,154 data records, each representing a single gold nanoparticle synthesis article, filtered from a database of 4,973,165 publications. Each record contains codified synthesis protocols and extracted morphological information from a total of 7,608 experimental and 12,519 characterization paragraphs.

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

How data science methods can improve the quality and efficiency of ICF and HEDP research

Data Science methods (many that are Bayesian based) are widely used in the physical sciences to estimate model parameters from experimental data, synthesize heterogeneous data, calibrate models, design experiments, and determine statistical significance of data. These methods provide a wealth of advantages over traditional analysis techniques because: 1) uncertainties are rigorously defined and propagated naturally through complex systems including covariance, 2) prior information is captured within the analysis framework (including rad-MHD and rad-hydro simulations), 3) competing models can be selected and/or ruled out using quantitative criteria, and 4) complex, heterogeneous data can be incorporated simultaneously. While these methods have been widely adopted as the gold standard in fields such as particle physics, astronomy, and biology, they have been slow to catch on in Inertial Confinement Fusion (ICF) and High Energy Density Physics (HEDP) research. Recently, several teams at LLNL, SNL, LANL, and the LLE have been exploring the use of these tools in their research and have found success. Here we propose that a concerted effort to consolidate these independent research efforts by developing and deploying common tools for use across the complex can revolutionize the way we approach data analysis, assimilation of theory and experiment, and decision making. The Bayesian formalism provides a means to accomplish this, but we are lacking certain infrastructure to make it happen on a large scale. Furthermore, once adopted, these techniques can be used to develop standards by which discoveries can be judged, similar to the so-called 5σ rule in high energy particle physics. Such standards may be used in the future to address the issue of unknown reproducibility in ICF and HED experiments caused by low shot rate and high cost per experiment. Our goals as a group are to advance the state of the art in HED measurement science by enabling: 1) better inferences from data with well-defined uncertainties, 2) better use of the data we have and continue to collect, 3) intelligent synthesis of data, 4) evaluation of the statistical significance of our data, and 5) informed decision making regarding the design of new experiments and instruments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2022

Digitalization, Management and Synthesis of Thermal Hydraulic Legacy Data Using Dynamical System Scaling

A unique attribute of the nuclear industry is the extent by which designers, developers, operators, and regulators pay attention to demonstrating the safety case. The safety case for nuclear systems is a written, traceable expression of safety, demonstrating the system’s protective measures against uncontrolled radiological releases. The nuclear installation resiliency or safety takes overriding priority over function and performance. The preparation of the safety case heavily relies on extensive experimental campaigns that challenge the target design under a spectrum of postulated accident scenarios. For safety reasons, these experiments are typically conducted in sub-scale test apparatuses intended to reproduce the postulated accident scenarios and event sequences realistically but subject to proven similarity criteria, often referred to as scaling analyses. Since the dawn of the nuclear industry, significant resources have been committed to the construction and operation of such test facilities and a massive amount of data has been generated. Such test data have been and are still used to validate the fundamental assumptions of nuclear power plant phenomena. As we move into the digital world, capturing this multi-decades worth of information in a transparent, easily accessible, and usable manner for the public and industry stakeholders is of paramount importance. Over the last few years FPoliSolutions has been actively developing an enterprise digital data ontology platform (FPoliDON) intended to do just that. The concept was introduced in a paper at the previous NURETH edition and has been significantly enhanced since. With the vision of helping designers use the data to assess their safety case evaluation models, the platform was designed not only to ontologically organize the experimental data, but also to guide the user in the complex analytics associated with the interpretation and use of such test results. A new feature which has been recently added is the ability for the user to access a library of sophisticated mathematical procedures for performing scaling analyses. For this purpose, the Dynamical System Scaling (DSS) procedure invented by Dr. Jose’ Reyes was formulated as one of the services in the digital platform. These methods have been demonstrated to provide an excelled mathematical apparatus to identify similarity criteria or quantify distortions between measured data and the target prototypical conditions. Moreover, DSS can provide a level of synthesis across SETs and IETs which has the promise to yield efficiency in the evaluation code assessment activities. This is often a daunting and expensive task to set up evaluation models that support the safety case of current and future nuclear power plants.

Reyes, Jose↗
DOE OSTI2022

Differentially Private Synthesis and Sharing of Network Data Via Bayesian Exponential Random Graph Models

Abstract Network data often contain sensitive relational information. One approach to protecting sensitive information while offering flexibility for network analysis is to share synthesized networks based on the information in originally observed networks. We employ differential privacy (DP) and exponential random graph models (ERGMs) and propose the DP-ERGM method to synthesize network data. We apply DP-ERGM to two real-world networks. We then compare the utility of synthesized networks generated by DP-ERGM, the DyadWise Randomized Response (DWRR) approach, and the Synthesis through Conditional distribution of Edge given nodal Attribute (SCEA) approach. In general, the results suggest that DP-ERGM preserves the original information significantly better than two other approaches in network structural statistics and inference for ERGMs and latent space models. Furthermore, DP-ERGM satisfies node DP through modeling the global network structure with ERGM, a stronger notion of privacy than the edge DP under which DWRR and SCEA operate.

graph synthesis↗
DOE OSTI2026

Heterogeneous Multi-Domain Dataset Synthesis to Facilitate Privacy and Risk Assessments in Smart City IoT

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures.

IoT↗
DOE OSTI2023

Temporal Dynamics of Place and Mobility

Despite variations in the population, climate, economics, politics, and culture, every country and city around the world shares the same time constraints: there are only 24 hours per day. Yet, the time-dependent activity patterns of when people interact with or move between public, private, and commercial locations change across space and across spatial scales. The temporal dynamics of a place reveal unique patterns based on the complex social, economic, and cultural interactions of humans across the built environment. The continued expansion of multi-modal temporal and geospatial data has attracted many disciplines to study temporal dynamics, each with its own interests, data, methods, and use cases. A comprehensive understanding of how the temporal patterns of a place are created, disrupted, and evolve is reliant on disciplines collaborating and sharing their unique perspectives. This chapter highlights ongoing work in this field and proposes core research questions that should be pursued with the appropriate collaboration and synthesis of data.

Sparks, Kevin↗
DOE OSTI2025

Developing a digital twin framework for remotely monitoring nuclear reactor facilities

A digital twin must seek to represent all applicable functional components of the system of interest. Different expertise is required for understanding the physical system being modeled than the skills needed for transforming those models into a functional digital twin through physics modeling, machine learning analysis, and visualization. The diversity of knowledge requires a multi-disciplinary team to ensure all system details are captured. Team members also need a method to verify that the data they generate within their domain can be effectively communicated to professionals in other fields. To address this challenge, this work provides an approach for developing a digital twin framework to remotely monitoring nuclear facilities. Through this, general knowledge of the framework is presented along with two examples to solidify the process. The AGN-201 digital twin and microreactor digital twins provide varying levels of complexity in a potential nuclear facility, where common threads are identified and lessons learned are provided. The goal of this research is to aid future researchers by providing a formula for a successful digital twin and in turn reducing the development time of nuclear system digital twins, specifically for remote monitoring.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Density Functional Theory Study of Epitaxially Strained Monolayer Transition Metal Chalcogenides for Piezoelectricity Generation

Two-dimensional transition metal chalcogenides (2D TMCs) are known for their wide range of bandgaps, flexibility, and high strength. Recent synthesis and data mining efforts indicate that 56 2D TMCs have low exfoliation energies and are relatively stable in monolayer form. Under epitaxial strain, we predict using density functional theory (DFT) calculations that the majority of these 2D TMCs can accommodate ±10% strain without breaking their crystal symmetry. The elastic and piezoelectric tensors indicate that 22 of 56 candidates are piezoelectric, and we derive their in-plane piezoelectric coefficient d 11 . The epitaxial strain is further predicted to enhance the d 11 by over 100% at 10% tensile epitaxial strain for most of these piezoelectric 2D TMCs. ReSe 2 at pristine state and Au 2 Se 2 at +5% epitaxial strain are predicted to obtain the extreme d 11 coefficients at -120 and 326 pm/V, respectively. Lastly, these findings have implications for the use of high-performance 2D piezoelectric materials in devices.

2D materials↗
DOE OSTI2025

Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies

A major goal of research in evolution and genetics is linking genotype to phenotype. This work could be direct, such as determining the genetic basis of a phenotype by leveraging genetic variation or divergence in a developmental, physiological, or behavioral trait. The work could also involve studying the evolutionary phenomena (e.g., reproductive isolation, adaptation, sexual dimorphism, behavior) that reveal an indirect link between genotype and a trait of interest. When the phenotype diverges across evolutionarily distinct lineages, this genotype-to-phenotype problem can be addressed using phylogenetic genotype-to-phenotype (PhyloG2P) mapping, which uses genetic signatures and convergent phenotypes on a phylogeny to infer the genetic bases of traits. The PhyloG2P approach has proven powerful in revealing key genetic changes associated with diverse traits, including the mammalian transition to marine environments and transitions between major mechanisms of photosynthesis. However, there are several intermediate traits layered in between genotype and the phenotype of interest, including but not limited to transcriptional profiles, chromatin states, protein abundances, structures, modifications, metabolites, and physiological parameters. Each intermediate trait is interesting and informative in its own right, but synthesis across data types has great promise for providing a deep, integrated, and predictive understanding of how genotypes drive phenotypic differences and convergence. We argue that an expanded PhyloG2P framework (the PhyloG2P matrix) that explicitly considers intermediate traits, and imputes those that are prohibitive to obtain, will allow a better mechanistic understanding of any trait of interest. Furthermore, this approach provides a proxy for functional validation and mechanistic understanding in organisms where laboratory manipulation is impractical.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

ESS-DIVE reporting format for leaf-level gas exchange data and metadata

Here we present documentation of the ESS-DIVE reporting format for leaf-level gas exchange data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-leaf-gas-exchange v1.0. The zip contains the reporting format description, guidelines, variable tables and instructions in GitHub markdown language (*.md) and 2 metadata templates as spreadsheets with drop down options (*.xlsx files, also function in GoogleSheets).

54 ENVIRONMENTAL SCIENCES↗