Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Task analysis”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Interaction Junk: User Interaction-Based Evaluation of Visual Analytic Systems

With the growing need for visualization to aid users in understanding large, complex datasets, the ability for users to interact and explore these datasets is critical. As visual analytic systems have advanced to leverage powerful computational models and data analytics capabilities, the modes by which users engage and interact with the information are limited. Often, users are taxed with directly manipulating parameters of these models through traditional GUIs (e.g., using sliders to directly manipulate the value of a parameter). However, the purpose of user interaction in visual analytic systems is to enable visual data exploration – where users can focus on their task, as opposed to the tool or system. As a result, users can engage freely in data exploration and decision-making, for the purpose of gaining insight. In this position paper, we discuss how evaluating visual analytic systems can be approached through user interaction analysis, where the goal is to minimize the cognitive translation between the visual metaphor and the mode of interaction (i.e., reducing the “Interactionjunk”). We motivate this concept through a discussion of traditional GUIs used in visual analytics for direct manipulation of model parameters, and the importance of designing interactions the support visual data exploration.

evaluation↗
DOE OSTI2024

A Provably Accurate Randomized Sampling Algorithm for Logistic Regression

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality approximations to both the estimated probabilities and the overall discrepancy of the model. Our analysis builds upon two simple structural conditions that boil down to randomized matrix multiplication, a fundamental and well-understood primitive of randomized numerical linear algebra. We analyze the properties of estimated probabilities of logistic regression when leverage scores are used to sample observations, and prove that accurate approximations can be achieved with a sample whose size is much smaller than the total number of observations. To further validate our theoretical findings, we conduct comprehensive empirical evaluations. Overall, our work sheds light on the potential of using randomized sampling approaches to efficiently approximate the estimated probabilities in logistic regression, offering a practical and computationally efficient solution for large-scale datasets.

Chowdhury, Agniva↗
DOE OSTI2016

Shared Freight Transportation and Energy Commodities Phase One: Coal, Crude Petroleum, & Natural Gas Flows

The Freight Analysis Framework (FAF) integrates data from a variety of sources to create a comprehensive picture of nationwide freight movements among states and major metropolitan areas for all modes of transportation. It provides a national picture of current freight flows to, from, and within the United States, assigns selected flows to the transportation network, and projects freight flow patterns into the future. The latest release of FAF is known as FAF4 with a base year of 2012. The FAF4 origin-destination-commodity-mode (ODCM) matrix is provided at national, state, major metropolitan areas, and major gateways with significant freight activities (e.g., El Paso, Texas). The U.S. Department of Energy (DOE) is interested in using FAF4 database for its strategic planning and policy analysis, particularly in association with the transportation of energy commodities. However, the geographic specification that DOE requires is a county-level ODCM matrix. Unfortunately, the geographic regions in the FAF4 database were not available at the DOE desired detail. Due to this limitation, DOE tasked Oak Ridge National Laboratory (ORNL) to assist in generating estimates of county-level flows for selected energy commodities by mode of transportation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

Advanced Ultrasupercritical (AUSC) Materials Thick-Walled Cycling Header Development for ComTest-AUSC (Final Report)

Alstom Power Inc., a wholly owned subsidiary of the General Electric Company (GE) recently completed “Advanced Ultrasupercritical (AUSC) Materials Thick-Walled Cycling Header Development for ComTest-AUSC” under Award No. DE-FE0026183 for the U.S. Department of Energy National Energy Technology Laboratory (DOE NETL). This project developed a design for testing of a high temperature nickel-based superalloy thick-walled header to simulate cycling and flexible plant operation in new and existing plants. The objective was to simulate the expected thermal stresses of a new AUSC plant or a retrofit header in an existing plant to improve flexible dynamic operation while maintaining component integrity and overall power plant reliability. The high performance superalloy materials that enable higher operating temperatures and pressures can support future transformational power technologies including new AUSC plants at 4250-5000 psi, 730°C/760°C or will serve as a replacement component in a repowered or retrofit USC coal powered cycling plant at 3600 psi, 600°C. These alloys can also enable supercritical CO2 cycles, nuclear power, solar power and more efficient natural gas combined cycle units as targeted by the U.S. DOE/NETL Cross-Cutting Research Technology Program Plan. Recently developed advanced nickel alloys such as Inconel 740H (740H) and Haynes 282 (H282) have been successfully tested on a pilot scale for fireside corrosion using steam loop tubing at AUSC temperatures of 760°C in actual operating coal fired boiler. These high strength materials have been demonstrated their adequacy for use in other high temperature boiler components such as superheater tubes. Thick-walled superheater outlet headers which operate at high temperatures and pressure under severe cycling condition are considered as one of the most critical component in the AUSC coal fired boiler. Over the design life of a plant, these headers are exposed to the most severe fatigue and creep damage accumulation and need to be properly designed for the expected cycling in an AUSC plant with flexible operation. These materials need to demonstrate adequate design life of 20 to 30 years for creep and fatigue conditions. The following tasks were performed: 1. A conceptual design was developed of a thick-walled component representative of typical headers with several tube connections and proper flow rates (~100,000 lbs/hr) suitable for Phase II test at 760°C operation. This was based on a process analysis of expected full scale new and component retrofit designs for AUSC; 2. A multi-physics flow and heat transfer computational fluid dynamics (CFD) analysis of the component for proper layout of the header configuration, number and geometry (size) of tubes required for bypass flow, flow rates through the tubing, and sectional pressure drops was performed. Steam to header metal heat transfer film coefficients in the header section and the tubes during the two stages (half load and full load) of the cycling operation were evaluated and used in the stress analysis; 3. Detailed analytical evaluation of the thick-walled pipe and tube connections for mechanical integrity under cycling conditions was performed. This included transient thermal stress analysis and fatigue usage estimation including the tube-to-pipe weld geometries and weld materials; 4. A layout and design of the thick-walled test component was developed and instrumentation required for a test was identified; 5. Continuum Damage Mechanics (CDM) creep prediction models were extended to 740H and H282 and validated with public data on 740H and H282 for use in creep analysis. In addition, detailed creep analyses were performed separately for the design life of the conceptual AUSC superheater outlet header using these General Electric (GE) creep constitutive models. GE was the prime contractor for the project, which was directed by Principal Investigator Robert Schrecengost and Lead Scientist Dr. B. Reddy Ganta. Other collaborative participants in this project included GE Global Research, GE Boiler Engineering, and a Technical Consultant from EPRI.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2023

Iodine Air Sampling: Installation and Preliminary Results

The F-Area wetlands are a known sink for radioiodine generated at the Savannah River Site (SRS). Wastewater containing iodine-129 ( 129 I) was discharged into the F-Area Seepage Basins for disposal, but the acidity of the wastewater mobilized some of the contaminants from the basin soil through the vadose zone and into the groundwater; ultimately creating a groundwater plume that extends towards Fourmile Branch and its associated wetland areas. Dissolved radioiodine specifically has migrated to the nearby F-Area wetlands, where it persists as a contaminant of concern. Accumulation and resurfacing of 129 I in the wetlands and nearby areas creates a potential secondary source zone where changes in biogeochemical conditions could result in the release of this sequestered 129 I to the air. Monitoring data has indicated that 129 I has a seasonal behavior at several surface water stations, with high concentrations during summertime and low concentrations during wintertime. Still, it is not known which factors control the seasonal behavior of 129 I. Further, it has been reported that 129 I contributed approximately 23% to the 2019 SRS offsite air pathway dose to the representative person for that year (SRNS, 2020), but air measurements of 129 I have never been taken in the F-Area wetlands. Because air emissions of 129 I from the wetlands have never been investigated, it is not known if this could be a potential release pathway that needs to be included in the site conceptual model. To address these unknowns, the Savannah River National Laboratory (SRNL) has an applied research task to investigate whether 129 I in the wetlands is emitted to the air and, if so, whether air concentrations exhibit seasonal variation. This report presents information about the system recently installed in the F-Area wetlands for measuring 129 I, as well as analysis results for samples collected June through August 2023. Quantification of this constituent will provide a better understanding of the contribution of airborne 129 I from the F-Area wetlands to the offsite dose and better inform ongoing and future remedial actions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Accelerating Science: A Computing Research Agenda

The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine natural phenomena through the computational lens, i.e., using algorithmic or information processing abstractions of the underlying processes; and our ability to acquire, share, integrate and analyze disparate types of data. However, there is a huge gap between our ability to acquire, store, and process data and our ability to make effective use of the data to advance discovery. Despite successful automation of routine aspects of data management and analytics, most elements of the scientific process currently require considerable human expertise and effort. Accelerating science to keep pace with the rate of data acquisition and data processing calls for the development of algorithmic or information processing abstractions, coupled with formal methods and tools for modeling and simulation of natural processes as well as major innovations in cognitive tools for scientists, i.e., computational tools that leverage and extend the reach of human intellect, and partner with humans on a broad range of tasks in scientific discovery (e.g., identifying, prioritizing formulating questions, designing, prioritizing and executing experiments designed to answer a chosen question, drawing inferences and evaluating the results, and formulating new questions, in a closed-loop fashion). This calls for concerted research agenda aimed at: Development, analysis, integration, sharing, and simulation of algorithmic or information processing abstractions of natural processes, coupled with formal methods and tools for their analyses and simulation; Innovations in cognitive tools that augment and extend human intellect and partner with humans in all aspects of science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Results of FY 2023 Alloy 617 and Alloy 709 High-Temperature Crack-Growth Testing

This report summarizes the work performed at Idaho National Laboratory under the “Creep, fatigue and creep-fatigue crack growth tests” task of the “Long-Term VHTR Material Qualification – INL” work package. Work was performed this year to validate the crack growth monitoring setup used with the test frames against both continuous crack length monitoring using an optical camera, as well as post-mortem analysis of marker bands on the fracture surface. Delays prevented the use of a gauge to measure load-line displacement for early tests, and so the creep-fatigue crack growth setup was evaluated using the actuator displacement for a load-line displacement setup. While not ideal, this allowed examination of the shortcomings with the current software setup that was originally designed for performing stress corrosion crack growth rate studies. The method for data collection was modified to link the crack growth monitoring software with Instron’s Wave Matrix software. Once the load-line displacement gauge arrived, the crack growth equipment was successfully modified to permit continuous monitoring of both load-line displacement and crack length. This is critical for creep-fatigue and creep crack growth rate studies for ductile material, as it permits the C* and Ct analyses. Test results are shown for Alloy 617 fatigue and creep-fatigue (without the gauge for load-line displacement measurements), as well as creep-fatigue of Alloy 709, which was performed with the completed test setup, allowing for crack length and load-line displacement monitoring.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Efficient Subset Simulation using Hamiltonian Neural Network enhanced Markov Chain Monte Carlo Methods

The Monte Carlo method delivers an unbiased estimate of the probability of failure. However, the variance of the estimate depends on the number of evaluated samples. This number must be very large for estimations of a low probability of failure. If the evaluation of each sample is computationally expensive, the crude Monte Carlo simulation strategy is impracticable. Therefore, subset simulations are used to reduce the required number of evaluations. Subset simulations require a Markov Chain Monte Carlo sampler, such as the random walk Metropolis-Hastings algorithm. The algorithm, however, struggles with sampling in low-probability regions, especially if they are narrow. As a consequence, advanced Markov Chain Monte Carlo simulations have been developed. In particular, the Hamiltonian Monte Carlo method explores the target distribution rapidly. Driven by the idea of Hamiltonian dynamics, this sampler provides a non-random walk through the target distribution. The incorporation of subset simulation and Hamiltonian Monte Carlo methods has shown promising results for reliability analysis. One downside of the Hamiltonian Monte Carlo method is that gradient evaluations are computationally expensive, especially when dealing with high-dimensional problems and evaluating long trajectories. We show that integrating Hamiltonian neural networks in Hamiltonian Monte Carlo simulations significantly speeds up the sampling task. Furthermore, the enhancement of adaptive trajectory length within the Hamiltonian Monte Carlo results in the efficient proposal of the following states. Based on this recent enhancement, we provide a fast sampling strategy for subset simulations using Hamiltonian neural networks to replace the evaluation of the gradient and significantly speed up the Hamiltonian Monte Carlo simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Can Large Language Models Understand Intermediate Representations?

Intermediate Representations (IRs) are essential in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. This paper presents a pioneering empirical study to investigate the capabilities of LLMs, including GPT-4, GPT-3, Gemma 2, LLaMA 3.1, and Code Llama, in understanding IRs. We analyze their performance across four tasks: Control Flow Graph (CFG) reconstruction, decompilation, code summarization, and execution reasoning. Our results indicate that while LLMs demonstrate competence in parsing IR syntax and recognizing high-level structures, they struggle with control flow reasoning, execution semantics, and loop handling. Specifically, they often misinterpret branching instructions, omit critical IR operations, and rely on heuristic-based reasoning, leading to errors in CFG reconstruction, IR decompilation, and execution reasoning. The study underscores the necessity for IR-specific enhancements in LLMs, recommending fine-tuning on structured IR datasets and integration of explicit control flow models to augment their comprehension and handling of IR-related tasks.

Jiang, Hailong↗
DOE OSTI2025

AI foundation models for experimental fusion tasks

Artificial Intelligence (AI) foundation models, while successful in various domains of language, speech, and vision, have not been adopted in production for fusion energy experiments. This brief paper presents how AI foundation models can be used for fusion energy diagnostics, enabling, for example, visual automated logbooks to provide greater insights into chains of plasma events in a discharge, in time for between-shot analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Cs-137 Body-burdens in Wild Pigs on the Department of Energy's Savannah River Site: Determination of an Appropriate Framework for Spatiotemporal Analysis of Hunter Dose Tracking System Data

The Savannah River Site (SRS) is a U.S. Department of Energy facility with an area of 310 square miles. Since its opening in 1951, SRS has produced and processed nuclear materials for national defense and industrial purposes. These activities have resulted in the release of radiocesium (Cs-137) which has led to contamination of associated streams, floodplains, man-made reservoirs, soil and vegetation. Contamination is spread to wild pigs as they move about SRS, consume vegetation, drink water and wallow in soil. SRS provides annual deer hunts open to the public in which wild pigs may be taken as well. The Cs-137 concentration of each animal is determined before it is released to the hunter for consumption and a cumulative dose is retained for each individual hunter. These tasks are accomplished using the Hunter Dose Tracking System (HDTS). The data collected on SRS by the HDTS for White-Tailed deer has been extensively analyzed. A repeatable method for calculating the background radiocesium body burden for the SRS deer population was established by Gaines and Novak (2009,2016). This model was framed using Cs-137 contamination obtained via gamma over-flight data, the decay rate of radiocesium and the historical behavior observed in the SRS deer population. These parameters were used in a spatiotemporal analysis to estimate background Cs-137 body burdens (from global fallout, etc.), as well as the effect SRS activities may have. The purpose of this project is to modify the framework to represent the behavior of the wild pig population on SRS such that HDTS data collected may be analyzed similarly. The 2018 data were collected via the HDTS in the field to screen wild pigs dispatched by Forest Service - Savannah River. These data will provide insight for an analysis of seasonal variance in the body burdens of the SRS wild pig population. The HDTS quickly provided Cs-137 concentrations in the field, as well as automatically updated the electronic database. Space: Historically, the average home range of wild pigs on the Savannah River Site has been reported at approximately six square miles. This range was confirmed accurate as of July 2018, with the observed minimum and maximum at one and ten square miles respectively. This range was geoprocessed using ArcMap to project buffers based on 1991 gamma over-flight data. The buffers were constructed to originate at all Cs-137 contamination point coordinates and protrude outward in all directions for 1.5, 2.45 and 6 square miles. While the 2.45 mile buffer is theoretically representative of potential hog exposure based on the average home range, it does not consider the possibility of home ranges not uniform in shape. Therefore, the 1.5 and 6 mile buffers were also constructed as visual aids for determining the appropriate hunt compartments for background level considerations. The SRS is divided into numbered hunt compartments of varying size and shape. These compartment numbers are used during sponsored hunts to record the location in which a wild pig was dispatched. Time: The Gaines report determined deer analysis should be carried out at the half-decade level for the best representation of true body burdens. This is due to the impact natural factors, such as drought or flood, can have on the bioaccumulation of Cs-137. To determine the appropriate time scale for hog analysis, all available data was totaled and averaged at 1, 2, 3 and 7 year increments. The annual averages and running average are provided. The appropriate compartments for the determination of the SRS hog population background level should be: 2, 3, 4, 9, 10, 14 and 15. Hogs dispatched within other compartments are most likely exposed to Cs-137 contamination and should not be included in the background level. The current dataset is small, but was still able to demonstrate the relationship between where hogs on SRS and the Cs-137 body burdens they incur. Geoprocessing the data using the average home range of wild pigs highlighted the importance of collecting additional samples from areas surrounding SRS if an accurate background is to be determined. When the average concentration of Cs-137 in pigs from background compartments is compared against that of pigs from contaminated compartments, a significant difference can be seen. A Table provides a summary of the analyzed data from the 15 years processed thus far.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Addressing Market Issues in Electrical Power Systems with Large Shares of Variable Renewable Energy

This paper reports recent findings from IEA Wind TCP Task 25, which compiles international experiences and research related to large-scale integration of wind and other renewable energy. In the paper, we address the main challenges for market integration of variable renewable energy, relating to price formation, cost recovery, balancing and other grid services. The paper gives an overview of recent scenario studies on electricity price impacts of (1) various generation, energy storage and demand types in different markets, and (2) different market designs and energy/climate policies. Studying markets with very high shares of variable renewable energy requires an improved set of analysis tools for forecasting market outcomes, estimating flexibility needs and sources, and assessing resource adequacy. Key market features need to be investigated within these improved analytical capabilities for systems transitioning to high shares of variable renewable energy, storage and flexible demand. System services that can be supported by markets will likely need to be revisited. Finally, this paper identifies open questions and suggested future market design work for supporting systems with very high shares of variable renewable energy, which are to be addressed in follow-up work of Task 25 collaborative research.

cost recovery↗
DOE OSTI2016

A Deep Generative Deconvolutional Image Model

A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic unpooling is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-layer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, to infer the latent features, and the top-layer features are connected with the max-margin classifier for discrimination tasks. The model is efficiently trained using a Monte Carlo expectation-maximization (MCEM) algorithm; the algorithm is implemented on graphical processor units (GPU) to enable large-scale learning, and fast testing. Excellent results are obtained on several benchmark datasets, including ImageNet, demonstrating that the proposed model achieves results that are highly competitive with similarly sized convolutional neural networks.

convolutional neural network↗
DOE OSTI2019

Highly Efficient Separation/Recycling Palladium(II) Ions from Aqueous Solutions by Silica Gel-Coated Graphene Oxide Modified with Mercapto Groups

The separation and recovery of precious metals from secondary resources is an extremely important and challenging task. Herein, a novel sorbent is successfully prepared by chemical grafting and utilized for the extraction and separation of Pd(II) ions from aqueous solutions. Various characterization techniques, including Fourier transform infrared spectroscopy, scanning electron microscopy, Brunauer–Emmett–Teller, X-ray photoelectron spectroscopy, and element analysis, are employed to study the structure, morphology, porous nature, and chemical composition of the as-prepared SiO{sub 2}@GO-SH nanocomposites. The adsorption of Pd(II) ions on the SiO{sub 2}@GO-SH can be described by the pseudo-second-order model and Langmuir isotherm model. The adsorption equilibrium has been attained within 90 min and the maximum adsorption capacity of 423.2 mg g{sup −1} was attained at pH 3.5 and T = 308 K. The results reveal that SiO{sub 2}@GO-SH exhibits an excellent adsorption performance towards Pd(II) ions. In addition, we have proposed the adsorption mechanism for Pd(II) ions on the SiO{sub 2}@GO-SH surface. The adsorption of Pd(II) in SiO{sub 2}@GO-SH is a chemisorption process, where partial adsorbed Pd(II) ions are reduced to Pd(0) by functional groups (-SH) in the SiO{sub 2}@GO-SH. The results indicate that SiO{sub 2}@GO-SH can serve as a promising sorbent for the efficient separation and recovery of palladium from the palladium-containing secondary resources.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

14 SOLAR ENERGY↗
DOE OSTI2017

Contaminant source identification using semi-supervised machine learning

Identification of the original groundwater types present in geochemical mixtures observed in an aquifer is a challenging but very important task. Frequently, some of the groundwater types are related to different infiltration and/or contamination sources associated with various geochemical signatures and origins. The characterization of groundwater mixing processes typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. In this paper, we propose a new contaminant source identification approach that performs decomposition of the observation mixtures based on Non-negative Matrix Factorization (NMF) method for Blind Source Separation (BSS), coupled with a custom semi-supervised clustering algorithm. Our methodology, called NMFk, is capable of identifying (a) the unknown number of groundwater types and (b) the original geochemical concentration of the contaminant sources from measured geochemical mixtures with unknown mixing ratios without any additional site information. NMFk is tested on synthetic and real-world site data. Finally, the NMFk algorithm works with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗
DOE OSTI2024

PhysBERT: A text embedding model for physics scientific literature

The specialized language and complex concepts in physics pose significant challenges for information extraction through Natural Language Processing (NLP). Central to effective NLP applications is the text embedding model, which converts text into dense vector representations for efficient information retrieval and semantic analysis. In this work, we introduce PhysBERT, the first physics-specific text embedding model. Pre-trained on a curated corpus of 1.2 × 106 arXiv physics papers and fine-tuned with supervised data, PhysBERT outperforms leading general-purpose models on physics-specific tasks, including the effectiveness in fine-tuning for specific physics subdomains.

Hellert, Thorsten (ORCID:0000000227970926)↗