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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS

Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.

Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗
DOE OSTI2026

Bioproduction, bioprotection, and biocontainment in multi-kingdom microbial systems with 3D spatial control

Engineered living materials (ELMs) are a class of hybrid materials that include engineered microbes encapsulated by a polymer matrix. The biotic and abiotic components define the ELMs design space and can be altered to improve performance and function. While current synthetic materials in the field display robust biocompatibility with both native and engineered living systems, we have a limited understanding of how to leverage three-dimensional (3D) form factors to spatially organize and control microbial dynamics within the material. Motivated by this knowledge gap, we employed extrusion-based 3D printing to fabricate multi-kingdom hydrogel constructs for the encapsulation of both single and multi-kingdom microbial systems. Core–shell cubic constructs enabled the spatial organization of a constitutive multi-kingdom system of levodopa (L-DOPA)-producing E. coli and betaxanthins (BXN)-producing S. cerevisiae. This spatial organization in 3D materials can introduce precise control over bioproduction, bioprotection, and biocontainment features that are critical to the efficacy of current ELMs. The relative spatial organization of the organisms, as well as the surface area-to-volume ratio were investigated to determine how these design elements impact microbial behavior (metabolite production, growth, expression, and cell distribution) over time. We demonstrated that F127-bis-urethane methacrylate (F127-BUM) core–shell geometries enable the hierarchical 3D printing of multi-kingdom constructs, offering customizable control over bioproduction, bioprotection, and biocontainment. With the optimization of these core–shell structures for continuous bioproduction, these ELMs could be deployed as compact and sustainable bioreactors in remote environments.

additive manufacturing↗
DOE OSTI2024

Information-incorporated gene network construction with FDR control

Abstract Motivation Large-scale gene expression studies allow gene network construction to uncover associations among genes. To study direct associations among genes, partial correlation-based networks are preferred over marginal correlations. However, FDR control for partial correlation-based network construction is not well-studied. In addition, currently available partial correlation-based methods cannot take existing biological knowledge to help network construction while controlling FDR. Results In this paper, we propose a method called Partial Correlation Graph with Information Incorporation (PCGII). PCGII estimates partial correlations between each pair of genes by regularized node-wise regression that can incorporate prior knowledge while controlling the effects of all other genes. It handles high-dimensional data where the number of genes can be much larger than the sample size and controls FDR at the same time. We compare PCGII with several existing approaches through extensive simulation studies and demonstrate that PCGII has better FDR control and higher power. We apply PCGII to a plant gene expression dataset where it recovers confirmed regulatory relationships and a hub node, as well as several direct associations that shed light on potential functional relationships in the system. We also introduce a method to supplement observed data with a pseudogene to apply PCGII when no prior information is available, which also allows checking FDR control and power for real data analysis. Availability and implementation R package is freely available for download at https://cran.r-project.org/package=PCGII.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A deep learning approach to identify missing is-a relations in SNOMED CT

Abstract Objective SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT. Materials and Methods Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Smart manufacturing maturity models and their applicability: a review

The purpose of this paper is to review existing smart manufacturing (SM) maturity models' dimensions and maturity levels to assess their applicability and drawbacks. There are many maturity models available but many of them have not been validated or do not provide a useful guide or tool for applications. This gap creates the need for a review of the existing maturity model's applicability. Nineteen peer-reviewed maturity models related to “Digital Transformation,” “Industry 4.0” or “Smart Manufacturing” were selected based on a systematic literature review and five consulting firm models were selected based on the author's industry knowledge. The chosen models were analyzed to determine 10 categories of dimensions. Then they are assessed on a 1–5 scale for how applicable they are in the 10 categories of dimensions. The five “consulting firm” models have a first-mover advantage, are more widely used in industry and are more applicable, but some require payment, and they lack published details and validation. The 19 “peer reviewed” models are not as widely used, lack awareness in the industry and are not as easy to apply because of no web tool for self-assessment, but they are improving. The categories defined to characterize the models and facilitate comparisons for users include “Information Technology (IT) and Cyber-Physical System (CPS) and Data,” “Strategy and Organization,” “Supply Chain and Logistics,” “Products and Services,” “Culture and Employees,” “Technology and Capabilities,” “Customer and Market,” “Cybersecurity and Risk,” “Leadership and Management” and “Governance and Compliance.” The analyzed maturity models were particularly weak in the areas of cybersecurity, leadership and governance. Researchers and practitioners can use this review with consideration of their specific needs to determine if a maturity model is applicable or if a new model needs to be developed. The review can also aid in the development of maturity models through the discussion of each of the dimension categories. Finally, compared to existing reviews of SM maturity models, this research determines comprehensive dimension categories and focuses on applicability and drawbacks.

42 ENGINEERING↗
DOE OSTI2020

Multichannel spectral estimation in acoustics: A state-space approach

Spectral estimation is a necessary methodology to analyze the frequency content of noisy data sets especially in acoustic applications. Many spectral techniques have evolved starting with the classical Fourier transform methods based on the well-known Wiener-Khintchine relationship relating the covariance-to-spectral density as a transform pair culminating with more elegant model-based parametric techniques that apply prior knowledge of the data to produce a high-resolution spectral estimate. Multichannel spectral representations are a class of both nonparametric, as well as parametric, estimators that provide improved spectral estimates. In any case, classical nonparametric multichannel techniques can provide reasonable estimates when coupled with peak-peaking methods as long as the signal levels are reasonably high. Furthermore, parametric multichannel methods can perform quite well in low signal level environments even when applying simple peak-picking techniques. In this paper, the performance of both nonparametric (periodogram) and parametric (state-space) multichannel spectral estimation methods are investigated when applied to both synthesized noisy structural vibration data as well as data obtained from a sounding rocket flight. It is demonstrated that for the multichannel problem, state-space techniques provide improved performance, offering a parametric alternative compared to classical methods.

42 ENGINEERING↗
DOE OSTI2020

Accelerating Learning with Set-Based Concurrent Engineering

The traditional design approach for product development is to develop a design based on customer requirements and technical knowledge, build the product according to the detail requirements provided, and then test the product to validate that it works as intended. The team typically starts by identifying a single design approach and spends their time validating a single design during testing. Design teams often encounter issues in the course of development with performance, manufacturability, interfaces and more. To compensate for test failures, teams often build in time into the schedule for additional design loopbacks. Further, when the loopbacks lead to change, the late changes are costly and risky, causing the team to focuses on "fixing the bare minimum" to meet cost and schedule expectations.

42 ENGINEERING↗
DOE OSTI2020

Grain boundary structure of two-dimensional tellurium revealed by 4D STEM

The recent realization of 2D tellurium (tellurene) has allowed scientists to demonstrate an air-stable monoelemental van der Waals material with remarkable potential for applications in electronics and optoelectronics. The chiral-chain structure of ultrathin Te allows controllable electronic and optical properties due to the non-monotonic carrier mobility and an increase in electronic band-gap as the thickness is reduced. Scalable and accurate production of 2D Te-based functional devices will require detailed knowledge of structural defects and strain field distributions that can be linked to transport properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Basin-specific geochemistry to promote unconventional efficiency

This project focused on two strategic geochemistry-based research thrusts and provided new knowledge in the following areas: (i) mitigation of scale precipitation in shale reservoirs; and (ii) improvement of hydrocarbon extractions from tight matrix pore space. Additionally, we developed a new acoustic monitoring approach to nondestructively monitor shale alteration in-situ in the laboratory and subsurface.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

A Mid-Century Net-Zero Scenario for the State of Wyoming and its Economic Impacts

Clean hydrogen has the potential to help achieve 10% economy-wide emissions reductions by 2050 relative to 2005, promote energy security and resilience, and develop a new economy in the United States. In 2030, the hydrogen economy could create about 100,000 new jobs to build new capital projects and clean hydrogen infrastructure. The Wyoming Energy Authority recently announced the state’s energy strategy, which establishes a goal of net-zero emissions by 2050. Under all likely scenarios, achieving a mid-century net-zero target will pose challenges and create opportunities for Wyoming’s energy sector. If executed properly, the transition could favorably affect the state’s economy overall in the long term. This research program examines the economic impact of fossil energy production in Wyoming and provides various predictions for future energy mixes to achieve net-zero emissions. Preliminary work suggests that Wyoming-based hydrogen production could have significant economic benefits and job creation implications for Wyoming. This study further assesses Wyoming’s opportunities to create hydrogen-based industries, assess economic impacts, identify knowledge gaps and research needs, and create a Hydrogen Center of Excellence to accelerate commercialization and deployment. This project helped to understand Wyoming's areas of focus for research and development and identified its areas of strength and potential challenges in creating a hydrogen ecosystem. As a result of this study, we estimate that for blue hydrogen produced from coal and gas resources, the overall cost reduction will be driven mainly by the carbon-sequestration tax credit and the improvement in carbon capture. Mature technologies, like SMR and PSA, will make limited contributions. They have no or limited reductions from an additional capacity deployment in future costs. We also understand the importance of continued support from public and private sectors for Carbon Capture and Storage (CCS)-related research, development, and demonstration programs at federal and state levels. The successful and efficient production of blue hydrogen requires a unique blend of energy resources, geology, regulation, law, and infrastructure. Wyoming has the distinction of meeting all these demands. The team also estimates that the availability and command of water resources accessible for hydrogen production are crucial for developing new projects. Water treatment, use, and disposal after treatment will also make projects possible. Primarily, this is relevant for hydrogen made using renewable energy. Wyoming has one of the best wind resource capacity in the nation. Harnessing this resource is challenging due to limited transmission line availability. Hydrogen could become one of the solutions to the stranded resource problem, primarily if the water availability challenge is addressed. Using produced oil & gas water could help to solve the problem. A commonly cited barrier to the expansion of hydrogen markets is the cost associated with constructing new pipelines, which typically require large amounts of capital to develop. Wyoming already possesses much of the export infrastructure needed to connect Wyoming’s hydrogen production with major markets across the West Coast, Pacific Northwest, Midwest, and Front Range regions of the United States, where a large portion of Wyoming’s natural gas is already transported. In addition to transportation by pipeline, rail transportation of hydrogen has also proven feasible. Wyoming uses its extensive railway system to transport large amounts of coal to its export partners across the United States. By using cryogenic or compressed-gas cars, Wyoming has the potential to add hydrogen to its existing network of railroad energy exports. The same technology may also be applied to hydrogen transport via trucks traveling interstate highways. Wyoming’s workforce is ready to meet the demands of clean hydrogen development. Many of the skills and training needed for hydrogen production are the same skills already possessed by Wyoming’s oil & gas and coal workforce. Many government and industry leaders expect clean hydrogen and other low-carbon energy projects to generate significant job growth and to recruit many already-trained oil & gas and coal workers whose jobs may be displaced. As energy companies seek to penetrate the markets for Wyoming hydrogen production, there is a natural mutual benefit to Wyoming’s workers and companies seeking to launch projects with the assistance of a trained workforce. Wyoming’s university and community college system have adopted several programs to ensure that highly qualified engineers and other technically skilled employees continue to graduate with skills to support the development of hydrogen and other innovative energy projects moving forward. Throughout the project, stakeholder outreach and education took many forms, including meetings with several major companies in the industry, collaborating with local government organizations, educational organizations, and national laboratories, tribal outreach and engagement, the sponsoring of several hydrogen-focused projects in many departments throughout the University of Wyoming, and developing a collaboration with international universities. The products of these collaborations consist of working relationships with several companies in the industry, educational institutions, national labs, and local government, as well as strong connections with individuals who will play an essential role in the success of the Hydrogen Energy Research Center.

08 HYDROGEN↗
DOE OSTI2022

One fold, many functions—M23 family of peptidoglycan hydrolases

Bacterial cell walls are the guards of cell integrity. They are composed of peptidoglycan that provides rigidity to sustain internal turgor and ensures isolation from the external environment. In addition, they harbor the enzymatic machinery to secure cell wall modulations needed throughout the bacterial lifespan. The main players in this process are peptidoglycan hydrolases, a large group of enzymes with diverse specificities and different mechanisms of action. They are commonly, but not exclusively, found in prokaryotes. Although in most cases, these enzymes share the same molecular function, namely peptidoglycan hydrolysis, they are leveraged to perform a variety of physiological roles. A well-investigated family of peptidoglycan hydrolases is M23 peptidases, which display a very conserved fold, but their spectrum of lytic action is broad and includes both Gram- positive and Gram- negative bacteria. In this review, we summarize the structural, biochemical, and functional studies concerning the M23 family of peptidases based on literature and complement this knowledge by performing large-scale analyses of available protein sequences. This review has led us to gain new insight into the role of surface charge in the activity of this group of enzymes. We present relevant conclusions drawn from the analysis of available structures and indicate the main structural features that play a crucial role in specificity determination and mechanisms of latency. Our work systematizes the knowledge of the M23 family enzymes in the context of their unique antimicrobial potential against drug-resistant pathogens and presents possibilities to modulate and engineer their features to develop perfect antibacterial weapons.

Razew, Alicja↗
DOE OSTI2021

Optimizing Ventilation Using Low-Cost Sensors to Improve Health, Safety, and Energy Efficiency

Air is the primary carrier of hazards within a space, whether it be hazardous bi-products of research activities or airborne pathogens. As a result, building ventilation is the primary defense against unseen airborne hazards. Critical laboratory facilities already demand the need for effective mitigation of exposure to research-related, airborne hazards, providing a proving ground for effective ventilation strategies that optimize safety of occupants and reduce energy use. The heart of smart laboratory building operation is dynamic, analytics-based ventilation, which requires an intimate knowledge of building environmental conditions achieved through contaminant-detection systems. Unfortunately, currently available contaminant-detection solutions are expensive, elaborate systems that raise barriers for building managers on a limited budget. Through the successful deployment of a novel low-cost, modular sensor technology, we have developed a demand-control ventilation protocol effective in improving safety and reducing energy in the critical laboratory environment. In this session, we will highlight best practices and lessons learned through this deployment that can be applied beyond laboratories without breaking the bank. This paper describes a low-cost solution for providing a safe, healthy building environment and reducing energy use through effective, efficient ventilation.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2024

Cybersecurity Lessons Learned from Vehicle to Grid Engagement

As the transportation industry continues to become electrified, introduction of additional digital devices within associated actions such as recharging bring additional potential for cybersecurity attacks. Devices that are designed, implemented, and operated with cybersecurity as a crucial consideration exacerbate these concerns by failing to provide strict boundaries on access to and use of the equipment. Emerging use cases such as Vehicle to Grid (V2G) charging may expand the potential physical effects of a cybersecurity attack by providing indirect access to electrical components of a building microgrid or portions of the larger power grid. This paper serves as an overview of findings and recommendations based on cybersecurity testing performed at a V2G implementation site operated by a member of the Memorandum of Understanding (MOU) to Establish the Vehicle-to-Everything (V2X) Collaboration [1]. The Department of Energy Office of Cybersecurity, Energy Security, and Emergency Response is a signatory of the MOU, and has funded this research paper and associated body of work regarding V2X cybersecurity. Sandia has a large background of previous research focused on Electric Vehicle (EV) cybersecurity, such as reference [2], which includes an overall survey of EV infrastructure cybersecurity and recommendations based on those findings. This report seeks to expand knowledge of EV cybersecurity status and needs by focusing on a specific implementation of V2G charging, and providing recommendations based on the relevant findings. This report serves as a publicly available, sanitized description of applied vulnerability testing on an operational V2G implementation. A more in-depth technical version of the report is provided to the MOU partner, but not available at the time of writing due to inclusion of proprietary information. V2G charging comes with many research problems that must be solved before the technology can securely implemented in sites with unrestricted public access or where cybersecurity attacks could have increased consequences, such as government offices. V2G charging requires many stakeholders such as end users, host sites, equipment vendors, and integrators, which all rely on operational safety and security as well as security and trustworthiness of any associated financial transactions.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Screening of metal complexes and organic solvents using the COSMOSAC-LANL model to enhance the energy density in a non-aqueous redox flow cell: an insight into the solubility

In this paper, we have proposed a first-principles methodology to screen transition metal complexes against a particular organic solvent and organic solvents against a particular transition metal complex based on their solubility information without the knowledge of heat of fusion and melting temperature. The energy density of a non-aqueous redox flow cell directly depends on the solubility of the redox active species in the non-aqueous medium. Here, we have used the “COSMOSAC-LANL” activity coefficient model which is based on first-principles COSMO calculations where the microscopic information is passed to the macroscopic world via a dielectric continuum solvation model, followed by a post-statistical thermodynamic treatment of the self-consistent properties of the solute particle to calculate the solubility. To model the activity coefficient at infinite dilution for the binary mixtures, a 3-suffix Margules (3sM) function is introduced for the quantitative estimation of the asymmetric interactions and, for the combinatorial term, the Staverman–Guggenheim (SG) form is used. The new activity coefficient model is separately called the “LANL” activity coefficient model. The metal complex and the organic solvent have been treated as a simple binary mixture. The present model has been applied to a set of 14 different organic solvents and 16 different transition metal complexes. Using the new LANL activity coefficient model in combination with the ADF-COSMOSAC-2013 model, we have shown how one can improve the solubility of a transition metal complex in an organic solvent. We applied our model to screen 84 binary mixtures to predict the compatible pair of redox active species and organic solvent to increase the energy density. The solvation mechanism of the transition metal complexes in the organic solvents was obtained using the new model. The results have been compared with the experimental and theoretical results where they are available.

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

The role of peer review and science for advancing public understanding

We live in a time of rapid change. The Covid-19 pandemic has united the world to understand and develop solutions to protect the health and wellbeing of society. At the same time, access to information on the internet and social media has exploded. People are exposed to information with many claims. Some of the information is relevant and accurate. A significant fraction may be inaccurate, biased, and potentially dangerous. With Covid-19, the information might point to cures that are toxic or risks that are not relevant. The scientific community has developed a process to develop and convey knowledge. The validation of results and their interpretation is critical to advancing knowledge. The core of the scientific process is based on developing a hypothesis-driven concept, designing and executing an experimental approach to prove or disprove the hypothesis, conducting a statistical analysis of the results to confirm their validity, and interpreting their meaning and impact. When their work is completed the scientist prepares to publish the work. Publication requires peer review. Hypotheses, experimental approaches, results, and interpretation of results are independently and anonymously reviewed by a scientific peer. The reviewers are selected based on their expertise in the field. The peer review process is essential for the integrity of the scientific process. Without a robust scientific process, society cannot discriminate between validated results and interpretations and biased, inaccurate, and potentially dangerous conjecture. With inaccurate science society and policy makers may make poor decisions that could hurt people and the world around them. While scientists should not censor information available to the public, scientists have a responsibility to inform the public. We must articulate what the scientific process is and why it is essential when considering the validity of information. Scientist are responsible for pointing out when public information has not been vetted through a scientific process and therefore cannot be considered conclusive. Scientists are responsible for highlighting information that has been generated and vetted with the scientific process. Over time, our goal is for the public to value the scientific process based on peer review. The public could become enlightened to the necessity for peer review to support decisions and action. Scientists need to contribute to advancing public knowledge. When invited to serve as a peer reviewer, scientists have a responsibility to contribute to the process. We all are extremely busy making peer review a distraction. While peer review does not directly benefit our own work and career, it benefits our chosen field of science. This is very important for the research area that our journal, Water-Energy Nexus, is focusing on. We cover the convergence of science and technology integrating across large spatial ranges (micro to global) in environmental pollution and the balance of remediation issues with energy use. Water-Energy Nexus wants to provide scientists with a platform for scientific debate and discussion on this rich and complicated topic. With the manuscripts submitted to WEN cover integrated research topics across a broad range of subjects from environment sciences through energy engineering, the role of peer review is essential. Peer review on a new submission to WEN should not be biased towards a specific science field. It should be done considering the complex nature of issues in the water-energy nexus. Scientists need to contribute to the public dialog. To accomplish these goals, we need to be honest, accurate, articulate, and use understandable language. The impact of WEN is very diverse affecting society in many ways. The scientific audience for WEN is diverse and readers may not fully understand the content of the papers outside their research expertise. Therefore, scientists, especially those who want to contribute to WEN, should work with communications specialists to convey message to benefit society. The audience includes the general public and researchers outside the author’s core specialty. While the peer-reviewed journal article is the cornerstone of our careers and critical for advancing scientific knowledge, a general interest story could benefit society even more. The author cannot lose sight that our work has two audiences, the science community, and more importantly, our family, friends, neighbors, and policy makers.

54 ENVIRONMENTAL SCIENCES↗
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 OSTI2024

Digital Analytics, Causal Knowledge Acquisition and Reasoning for Technical Language Processing

Complex engineering systems such as nuclear power plants (NPPs) generate and collect large amounts of equipment reliability (ER) data elements that contain information on the status of components, assets, and systems. Some of this information is textual in form and can be found in documents such as incident reports (IRs) and work orders (WOs). Analyses of textual data in current NPPs-using natural language processing (NLP) methods-have been expanded over the last decade, and it is only recently that the true potential of such analyses has emerged. So far, applications of NLP methods have mostly been limited to classification and prediction, the goal being to identify the nature of the textual element (e.g., safety or non-safety related). Here, we target a more complex problem: automatically extracting knowledge from a textual element in order to assist system engineers in conducting system health assessments. Knowledge extraction is a very broad concept, and its definition may vary depending on the application context. Our methods are a blend of both rule-based and machine learning (ML) algorithms. For our purposes, knowledge extraction means identifying the systems or assets mentioned in a given textual element, as well as the type of event described (e.g., component failure or maintenance activity). In addition, we want to capture details such as measured quantities and the temporal/cause-effect relations between events. In this tool, we also demonstrate how textual data elements are preprocessed in order to handle typos, acronyms, and abbreviations. One main feature of these methods is that they are not based solely on data, but are in fact model-based. In other words, they also rely on MBSE models that are designed to capture-from a functional point of view-the architecture of the systems/assets under consideration. The main purpose of such models is to digitally emulate system engineers' knowledge of system and asset architecture and to identify dependencies among systems, assets, and components. Provided these models, analyses of textual and numeric ER data can be performed by first identifying the OPM model elements to which the ER data elements are referring. The relationships between ER data elements are then identified by checking for any temporal or logical dependencies.

Mandelli, Diego [Idaho National Laboratory (INL), ↗
DOE OSTI2025

Digital Twin for Chemical Science: a case study on water interactions on the Ag(111) surface

Directly visualizing chemical trajectories offers insights into catalysis, gas-phase reactions and photoinduced dynamics. Tracking the transformation of chemical species is best achieved by coupling theory and experiment. Here we developed Digital Twin for Chemical Science (DTCS) v.01, which integrates theory, experiment and their bidirectional feedback loops into a unified platform for chemical characterization. DTCS addresses a core question: given a set of experimental conditions, what is the expected outcome and why? It consists of a forward solver that takes a chemical reaction network and predicts spectra under experimental conditions, and an inverse solver that infers kinetics from measured spectra. We applied DTCS to ambient-pressure X-ray photoelectron spectroscopy measurements of the Ag–H2O interface as an example. This approach enables real-time knowledge extraction and guides experiments until a stopping condition is met based on accuracy and degeneracy. As a step toward autonomous chemical characterization, DTCS provides mechanistic knowledge in a verified, standardized manner.

Chemistry↗