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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 OSTI2021

Development of a Framework for Data Integration, Assimilation, and Learning for Geological Carbon Sequestration (DIAL-GCS) (Final Report)

This project aimed to develop and demonstrate a Data Integration, Assimilation, and Learning framework for geologic carbon sequestration projects (DIAL-GCS). DIAL-GCS is an intelligence monitoring system (IMS) for automating GCS closed-loop management by leveraging recent developments in machine learning technologies, complex event processing (CEP), and reduced-order modeling. The safe and efficient operation of GCS repositories requires integrated monitoring to track the injected CO¬2 as it moves within a storage reservoir. GCS projects are data intensive, as a result of proliferation of digital instrumentation and smart-sensing technologies. GCS projects are also resource intensive, often requiring multidisciplinary teams performing different monitoring, verification, accounting (MVA) tasks throughout the lifecycle of a project to ensure secure containment of injected CO2. The success of GCS thus depends in a large part on our ability to access, assimilate, and analyze heterogeneous data and information sources in a timely manner. This project included a number of meaningful and necessary tasks to transform the human domain knowledge into machine-interpretable rules for automating knowledge extraction and discovery in GCS. The specific technical objectives of the proposed DIAL-GCS project were to develop an ontology-driven GCS data management module for storing, querying, and exchanging GCS data (both historic and live sensor data) from multiple sources and in heterogeneous formats. Incorporate a CEP engine for detecting abnormal situations by seamlessly combining expert knowledge, rule-based reasoning, and machine learning. Enable uncertainty quantification and predictive analytics using a combination of coupled-process modeling, AI/ML methods, and reduced-order modeling, and integrate and demonstrate the system’s capabilities with both real and simulated data. As far as we know, this is one of the first projects aimed to develop intelligent monitoring systems (IMS) targeting the GCS. Under this project, the team had developed a large number of web applications and scientific algorithms that contribute the main theme of intelligent monitoring. The team has published more than a dozen peer reviewed papers and disseminated the research results at multiple technical meetings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Knowledge Graph Approach to Analyze Systems and Assets Health

Nuclear power plants collect large amounts of equipment reliability data elements that contain information on the statuses of component, assets, and systems. All these data elements precisely record asset and system performance and health throughout the lifecycle of those assets and systems. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly focuses on the integration of numeric and textual data elements in order to assist plant system engineers in analyzing equipment reliability data. This task begins with preprocessing the data by extracting knowledge from textual data via natural language processing methods and quantifying system, asset, and component health based on numeric data. We then employed model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Data elements were then associated with a single MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2024

From Data to Knowledge: A Graph-Based Reliability Approach to Assess System Health

With the goal of maximizing plant reliability and availability, complex systems such as nuclear power plants continuously monitor and record the performance and the health status of many components, assets, and systems. Such data may take the form of online monitoring data, condition reports, and maintenance reports and it carries the potential to provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind them and to predict their direct consequences. The analysis of such data poses however few challenges. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly tackles these challenges, and it focuses on the integration of all these data elements in order to assist plant system engineers in analyzing component, assets, and systems performances and optimize maintenance activities. This is performed by 1) extracting knowledge from textual data via technical language processing methods, and 2) quantifying system, asset, and component health from numeric condition-based data. We rely on model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Numeric and textual data elements are then associated with an MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Recent progress toward catalyst properties, performance, and prediction with data-driven methods

Herein, data-driven approaches are currently renovating the field of heterogenous catalysis and open a door to advance catalyst design. Their success depends heavily on the synergy among machine learning (ML), experimental data, and quantum mechanical (QM) calculations. In this brief survey of recent progress, we examine catalysis informatics in the context of (1) ML-aided catalyst characterizations, (2) knowledge extractions from experimental data, (3) predictions of catalytic properties and constructions of reaction networks, and (4) ML-enabled large-scale QM simulations. An outlook on the current challenges of this rapidly evolving field is also provided.

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

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including simulation input generation, simulation output analysis and visualization, conducting error analysis, co-simulation, simulation knowledge extraction and training, and simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling.

building energy modeling↗
DOE OSTI2020

Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on an extensive computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C–C bond scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM-Pt-Pt(111) and Pt-TM-Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. In conclusion, the approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of novel catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2024

To Derive or Not to Derive: I/O Libraries Take Charge of Derived Quantities Computation

The ever-increasing volume of data produced by HPC simulations necessitates scalable methods for data exploration and knowledge extraction. Scientific data analysis often involves complex queries across distributed datasets, requiring manipulation of multiple primary variables and generating derived data that needs to be handled efficiently, creating challenges for applications that need to parse many large datasets. Relying on individual applications to handle all intermediate data generally leads to redundant computations across studies and unnecessary data transfers. In this paper, we investigate the performance of different approaches where applications define derived variables as quantities of interest (QoIs) and offload the computation and transfer of these QoIs to the I/O library. This significantly reduces redundancy and optimizes data movement across the distributed storage and processing infrastructure by allowing control over when and where derived variables are computed. We present a detailed analysis of the performance-storage trade-offs associated with different solutions and showcase results for our study on two large-scale datasets created from climate and combustion simulations.

Gainaru, Ana↗
DOE OSTI2024

Metagenomic clustering links specific metabolic functions to globally relevant ecosystems

ABSTRACT Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome’s functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute’s Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome’s metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

System and methods for automated detection, reasoning and recommendations for resilient cyber systems

A method for securing an IT (information technology) system using a set of methods for knowledge extraction, event detection, risk estimation and explanation for ranking cyber-alerts which includes a method to explain the relationship (or an attack pathway) from an entity (user or host) and an event context to another entity (a high-value resource) and an event context (attack or service failure).

Choudhury, Sutanay↗
DOE OSTI2023

K-RET: knowledgeable biomedical relation extraction system

Abstract Motivation Relation extraction (RE) is a crucial process to deal with the amount of text published daily, e.g. to find missing associations in a database. RE is a text mining task for which the state-of-the-art approaches use bidirectional encoders, namely, BERT. However, state-of-the-art performance may be limited by the lack of efficient external knowledge injection approaches, with a larger impact in the biomedical area given the widespread usage and high quality of biomedical ontologies. This knowledge can propel these systems forward by aiding them in predicting more explainable biomedical associations. With this in mind, we developed K-RET, a novel, knowledgeable biomedical RE system that, for the first time, injects knowledge by handling different types of associations, multiple sources and where to apply it, and multi-token entities. Results We tested K-RET on three independent and open-access corpora (DDI, BC5CDR, and PGR) using four biomedical ontologies handling different entities. K-RET improved state-of-the-art results by 2.68% on average, with the DDI Corpus yielding the most significant boost in performance, from 79.30% to 87.19% in F-measure, representing a P-value of 2.91×10−12. Availability and implementation https://github.com/lasigeBioTM/K-RET.

Sousa, Diana F. (ORCID:0000000305979273)↗
DOE OSTI2024

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning

Abstract Motivation Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Results Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM’s native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. Availability and implementation SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structured information extraction from scientific text with large language models

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Nanomaterial Synthesis Insights from Machine Learning of Scientific Articles by Extracting, Structuring, and Visualizing Knowledge

Nanomaterials of varying compositions and morphologies are of interest for many applications from catalysis to optics, but the synthesis of nanomaterials and their scale-up are most often time-consuming and Edisonian processes. Information gleaned from the scientific literature can help inform and accelerate nanomaterials development, but again, searching the literature and digesting the information are time-consuming manual processes for researchers. To help address these challenges, here we developed scientific article-processing tools that extract and structure information from the text and figures of nanomaterials articles, thereby enabling the creation of a personalized knowledgebase for nanomaterials synthesis that can be mined to help inform further nanomaterials development. Starting with a corpus of ~35k nanomaterials-related articles, we developed models to classify articles according to the nanomaterial composition and morphology, extract synthesis protocols from within the articles’ text, and extract, normalize, and categorize chemical terms within synthesis protocols. We demonstrate the efficiency of the proposed pipeline on an expert-labeled set of nanomaterials synthesis articles, achieving 100% accuracy on composition prediction, 95% accuracy on morphology prediction, 0.99 AUC on protocol identification, and up to a 0.87 F1-score on chemical entity recognition. In addition to processing articles’ text, microscopy images of nanomaterials within the articles are also automatically identified and analyzed to determine the nanomaterials’ morphologies and size distributions. To enable users to easily explore the database, we developed a complementary browser-based visualization tool that provides flexibility in comparing across subsets of articles of interest. We use these tools and information to identify trends in nanomaterials synthesis, such as the correlation of certain reagents with various nanomaterial morphologies, which is useful in guiding hypotheses and reducing the potential parameter space during experimental design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗