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DOE OSTI2018

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 10 4 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

36 MATERIALS SCIENCE↗

DOE OSTI2019

A Streamlined and Standardized Benthic Habitat Mapping Approach for Marine and Hydrokinetic Site Environmental Assessments

We have combined two seafloor mapping techniques to efficiently map benthic habitat conditions. Multibeam echosounder (MBES) surveys provide detailed bathymetry and backscatter mosaics. Sediment profile and plan view imaging (SPI/PV) rapidly provides dense ground truth data on seabed physical and biological features. A key innovation of this project has been the development of a computer-assisted image processing system that standardizes the SPI data generation process. A unique combination of machine- and deep-learning techniques for grain-size classification, sediment–water interface delineation, and object detection (e.g., infauna and feeding voids) is described. An automated SPI image analysis system provides a standardized and cost-effective approach for image processing. Combining MBES with SPI/PV camera technology provides a first tier approach to rapidly documenting seabed physical and biological conditions in high resolution at MHK sites. When coupled with computer automated data extraction from the imagery, this approach provides a standardized and cost-effective method to establish and monitor benthic habitat conditions over time.

Benthic Habitat Maping, Sediment-Profile Imaging, ↗

DOE OSTI2020

Observation of interacting polaronic gas behavior in Ta-doped TiO 2 thin films via terahertz time-domain spectroscopy

Transparent conductive oxides (TCOs)—materials that have the twin desirable features of high optical transmission and electrical conductivity—play an increasingly significant role in the fields of photovoltaics and information technology. As an excellent TCO, Ta-doped anatase TiO 2 shows great promise for a wide range of applications. Here, terahertz time-domain spectroscopy is used to study the complex optical conductivity $\tilde{σ}$ (ω) of the TCO—heavily Ta-doped TiO 2 thin films with different Ta-doping concentrations, in the frequency range of 0.3–2.7 THz and the temperature range of 10–300 K. Fitting the complex optical conductivity to a Drude-like behavior allows us to extract the temperature dependence of the effective mass, which suggests the existence of many-body large polarons. Moreover, the carrier scattering rate of Ta-doped TiO 2 with different carrier concentrations agrees with the interacting polaron gas theory. Our results suggest that with increasing electron density in TiO 2 , the interaction between polarons is larger and electron–phonon coupling is smaller, which is beneficial for achieving high mobility and conductivity in TiO 2 .

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dust and the intrinsic spectral index of quasar variations: hints of finite stress at the innermost stable circular orbit

ABSTRACT We present a study of 9 242 spectroscopically confirmed quasars with multiepoch ugriz photometry from the SDSS Southern Survey. By fitting a separable linear model to each quasar’s spectral variations, we decompose their five-band spectral energy distributions into variable (disc) and non-variable (host galaxy) components. In modelling the disc spectra, we include attenuation by dust on the line of sight through the host galaxy to its nucleus. We consider five commonly used attenuation laws, and find that the best description is by dust similar to that of the Small Magellanic Cloud, inferring a lack of carbonaceous grains from the relatively weak 2175-Å absorption feature. We go on to construct a composite spectrum for the quasar variations spanning 700–8000 Å. By varying the assumed power-law Lν ∝ να spectral slope, we find a best-fitting value α = 0.71 ± 0.02, excluding at high confidence the canonical Lν ∝ ν1/3 prediction for a steady-state accretion disc with a T ∝ r−3/4 temperature profile. The bluer spectral index of the observed quasar variations instead supports the model of Agol & Krolik, and Mummery & Balbus, in which a steeper temperature profile, T ∝ r−7/8, develops as a result of finite magnetically induced stress at the innermost stable circular orbit extracting energy and angular momentum from the black hole spin.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Simulator Data Analysis to Inform Digitalized Environment Impacts on Human Reliability

The U.S. Nuclear Regulatory Commission (NRC) has developed a human reliability analysis (HRA) method, termed the Integrated Human Event Analysis System for Event and Condition Assessment (IDHEAS-ECA), in order to estimate human error probabilities (HEPs) in risk-informed regulatory applications. To update the quantification part of IDHEAS-ECA, the NRC required human performance and error data from fully digitalized main control rooms (MCRs); therefore, it requested that Idaho National Laboratory (INL) revisit previous data collection studies and investigate how the following three factors impact human reliability: self-checking, peer-checking, and automation. The HRA data collection studies revisited were the Human Reliability Data Extraction (HuREX) project, developed by the Korea Atomic Energy Research Institute (KAERI), and the Simplified Human Error Experimental Program (SHEEP), developed by INL. HuREX is a representative HRA data collection study that collects human reliability data from full-scope simulators staffed by licensed operators. SHEEP, on the other hand, has been proposed to complement such full-scope studies by collecting data via simplified simulators staffed by non-licensed student operators. In the HuREX study, KAERI collected HRA data from fully digitalized MCRs for the Advanced Power Reactor (APR)–1400. The SHEEP data were obtained from simplified simulators that partially mimicked the features of digitalized MCRs. The present report mainly discusses how the impacts of the aforementioned three factors on human errors were derived from these two data collection studies.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2016

Development of a Low-Level Ar-37 Calibration Standard

Argon-37 is an important environmental signature of an underground nuclear explosion. Producing and quantifying low-level 37Ar standards is an important step in the development of sensitive field measurement instruments for use during an On-Site Inspection, a key provision of the Comprehensive Nuclear-Test-Ban Treaty. This paper describes progress at Pacific Northwest National Laboratory (PNNL) in the development of a process to generate and quantify low-level 37Ar standard material, which can then be used to calibrate sensitive field systems at activities consistent with soil background levels. The 37Ar used for our work was generated using a laboratory-scale, high-energy neutron source to irradiate powdered samples of calcium carbonate. Small aliquots of 37Ar were then extracted from the head space of the irradiated samples. The specific activity of the head space samples, mixed with P10 (90% stable argon:10% methane by mole fraction) count gas, is then derived using the accepted Length-Compensated Internal-Source Proportional Counting method. Due to the low activity of the samples, a set of three Ultra-Low Background Proportional-Counters designed and fabricated at PNNL from radio-pure electroformed copper was used to make the measurements in PNNL’s shallow underground counting laboratory. Very low background levels (<10 counts/day) have been observed in the spectral region near the 37Ar emission feature at 2.8 keV. Two separate samples from the same irradiation were measured. The first sample was counted for 12 days beginning 28 days after irradiation, the second sample was counted for 24 days beginning 70 days after irradiation (the half-life of 37Ar is 35.0 days). Both sets of measurements were analyzed and yielded very similar results for the starting activity (~0.1 Bq) and activity concentration (0.15 mBq/ccSTP argon) after P10 count gas was added. A detailed uncertainty model was developed based on the ISO Guide to the Expression of Uncertainty in Measurement. This paper presents a discussion of the measurement analysis, along with assumptions and uncertainty estimates.

Absolute radiation measurement↗

DOE OSTI2025

Exploring diversion-pathway analysis of a generic molten-salt fast reactor using multiphysics informed signatures

Molten salt reactors are being explored by multiple commercial ventures due to their inherent safety features, flexibility in fuel sources, and high fuel utilization and thermal efficiency. The continual flow of fuel salt, large fissile quantities present, and ability to add or divert material due to the liquid nature introduces new challenges for international safeguards. To understand how international safeguards should be applied, it is important to capture the inherent multi-physics nature of a molten salt reactor. This work examines a generic molten salt fast reactor to understand how potential diversion scenarios would affect the concentration of radionuclides in the primary and auxiliary systems. Three types of diversion were examined: a slow drip of fuel salt, gaseous plutonium extraction, and uranium metal plating. The analysis determined that several key isotopes become statistically significant once diversion begins, indicating that detection of such diversion cases would be possible through measuring specific signatures such as gamma spectra.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2016

Estimating statistical isotropy violation in CMB due to non-circular beam and complex scan in minutes

Mild, unavoidable deviations from circular-symmetry of instrumental beams along with scan strategy can give rise to measurable Statistical Isotropy (SI) violation in Cosmic Microwave Background (CMB) experiments. If not accounted properly, this spurious signal can complicate the extraction of other SI violation signals (if any) in the data. However, estimation of this effect through exact numerical simulation is computationally intensive and time consuming. A generalized analytical formalism not only provides a quick way of estimating this signal, but also gives a detailed understanding connecting the leading beam anisotropy components to a measurable BipoSH characterisation of SI violation. In this paper, we provide an approximate generic analytical method for estimating the SI violation generated due to a non-circular (NC) beam and arbitrary scan strategy, in terms of the Bipolar Spherical Harmonic (BipoSH) spectra. Our analytical method can predict almost all the features introduced by a NC beam in a complex scan and thus reduces the need for extensive numerical simulation worth tens of thousands of CPU hours into minutes long calculations. As an illustrative example, we use WMAP beams and scanning strategy to demonstrate the easability, usability and efficiency of our method. We test all our analytical results against that from exact numerical simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Linear point and sound horizon as purely geometric standard rulers

The baryon acoustic oscillations feature (BAO) imprinted in the clustering correlation function is known to furnish us cosmic distance determinations that are independent of the cosmological-background model and the primordial perturbation parameters. These measurements can be accomplished rigorously by means of the purely geometric BAO methods. To date two different purely geometric BAO approaches have been proposed. The first exploits the linear-point standard ruler. The second, called correlation-function model-fitting, exploits the sound-horizon standard ruler. A key difference between them is that, when estimated from clustering data, the linear point makes use of a cosmological-model-independent procedure to extract the ratio of the ruler to the cosmic distance, while the correlation-function model-fitting relies on a phenomenological cosmological model for the correlation function. Nevertheless the two rulers need to be precisely defined independently of any specific observable (e.g., the BAO). We define the linear point and sound horizon and we fully characterize and compare the two rulers’ cosmological-parameter dependence. We find that they are both geometrical (i.e., independent of the primordial cosmological parameters) within the required accuracy, and that they have the same parameter dependence for a wide range of parameter values. We estimate the rulers’ best-fit values and errors, given the cosmological constraints obtained by the Planck Satellite team from their measurements of the cosmic microwave background temperature and polarization anisotropies. We do this for three different cosmological models encompassed by the purely geometric BAO methods. In each case we find that the relative errors of the two rulers coincide and they are insensitive to the assumed cosmological model. Interestingly both the linear point and the sound horizon shift by 0.5σ when we do not fix the spatial geometry to be flat in Λ CDM. Finally, this points toward a sensitivity of the rulers to different cosmological models when they are estimated from the cosmic microwave background.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation

Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Exploring Local Vibrational Structure in Protein-Bound Chlorophyll a: Isotope-Enrichment Experiments and Electrostatic Analysis

Local chlorophyll (Chl) vibrations play an essential role in biological photosynthesis by facilitating rapid energy transfer between pigments. In research studies, they also provide a useful spectroscopic probe of the local protein environment that surrounds each pigment. However, measuring the complete vibrational absorption spectrum of a protein-bound Chl molecule is much more difficult than, for example, Chl in neat solvent due to overlap with protein vibrations that typically drown out Chl vibrational features. Resonance Raman and fluorescence spectroscopies provide a way around this problem for Franck–Condon active vibrations, but these often rely on cryogenic measurement conditions and fail to capture vibrational signatures from, e.g., ester-group vibrations that lack coupling to a convenient electronic transition. In the present contribution, we use 13 C-enrichment of the protein backbone to shift protein background signals to lower frequency, providing a largely clean spectral window in which to study local Chl a C═O stretch modes. A room-temperature absorption spectrum for Chl a in the water-soluble chlorophyll protein (WSCP) of Lepidium virginicum is thus extracted as a difference between protein-plus-pigment and protein-only vibrational spectra. Excellent agreement in the molecular fingerprint region with the vibrational spectrum of Chl a in organic solvents confirms that the resulting spectrum represents the response of the protein-bound Chl a molecule. Furthermore, the ester group resonance is observed to shift in response to the S53P mutation that eliminates a 17 3 ester group hydrogen bond from the protein environment. Finally, we analyze these experimental results using MD-based electrostatic analysis, finding that electric-field mapping at the C atom of the ester group provides a satisfactory explanation for the observed frequency shifts between organic solvent and protein environment. MD analysis further suggests that a red-shifted ester peak observed experimentally for the S53P mutant results from solvation of the 17 3 Chl ester group due increase water penetration into the Chl-binding pocket relative to wild-type WSCP. Here, we anticipate that these results will prove useful both for benchmarking future simulation work and as a reference for interpreting Chl vibrational spectra as a probe of pigment–protein interactions.

14 SOLAR ENERGY↗

DOE OSTI2018

Origin of the Two Bands in the B800 Ring and Their Involvement in the Energy Transfer Network of Allochromatium vinosum

Bacterial photosynthesis features robust and adaptable energy-harvesting processes in which light-harvesting proteins play a crucial role. The peripheral light-harvesting complex of the purple bacterium Allochromatium vinosum is particularly distinct, featuring a double peak structure in its B800 absorption band. Two hypotheses—not necessarily mutually exclusive—concerning the origin of this splitting have been proposed; either two distinct B800 bacteriochlorophyll site energies are involved, or an excitonic dimerization of bacteriochlorophylls within the B800 ring takes place. Through the use of two-dimensional electronic spectroscopy, we present unambiguous evidence that excitonic interaction shapes the split band. We further identify and characterize all of the energy transfer pathways within this complex by using a global kinetic fitting procedure. Our approach demonstrates how the combination of two-dimensional spectral resolution and self-consistent fitting allows for extraction of information on light-harvesting processes, which would otherwise be inaccessible due to signal congestion.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Cyclotron instability in the afterglow mode of minimum-B ECRIS

It was shown recently that cyclotron instability in non-equilibrium plasma of a minimum-B electron cyclotron resonance ion source (ECRIS) causes perturbation of the extracted ion current and generation of strong bursts of bremsstrahlung emission, which limit the performance of the ion source. The present work is devoted to the dynamic regimes of plasma instability in ECRIS operated in pulsed mode. Instability develops in decaying plasma shortly after heating microwaves are switched off and manifests itself in the form of powerful pulses of electromagnetic emission associated with precipitation of high energy electrons. Time-resolved measurements of microwave emission bursts are presented. It was found that even in various gases (helium and oxygen were studied) and at different values of magnetic field and heating power, the dynamic spectra demonstrate common features: decreasing frequency within a single burst as well as from one burst to another.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2018

Shallow three-dimensional structure of the San Jacinto fault zone revealed from ambient noise imaging with a dense seismic array

In this work, we use 1 month of continuous seismic waveforms from a very dense seismic network to image with unprecedented resolution the shallow damage structure of the San Jacinto fault zone across the Clark fault strand. After calculating noise correlations, high apparent velocity arrivals coming from below the array are removed using a frequency–wavenumber filter. This is followed by a double-beamforming analysis on multiple pairs of subarrays to extract phase and group velocity information across the study area. The phase and group velocity dispersion curves are regionalized into phase and group velocity maps at different frequencies, and these maps are inverted using a neighbourhood algorithm to build a 3-D shear wave velocity model around the Clark fault down to ~500 m depth. The model reveals strong lateral variations across the fault strike with pronounced low-velocity zones corresponding to a local sedimentary basin and a fault zone trapping structure. The results complement previous earthquake- and seismic noise-based imaging of the fault zone at greater depth and clarify properties of structural features near the surface.

58 GEOSCIENCES↗

DOE OSTI2026

Machine learning for slow-extraction uniformity at the Fermilab Delivery Ring

The Muon-to-Electron Conversion Experiment (Mu2e) requires uniform slow extraction from the Fermilab Delivery Ring during each 43 ms spill. This project analyzed 13,737 spills from seven parquet files collected during March and June 2026 commissioning. Each spill contained 430 samples at 0.1 ms resolution and was summarized by the Spill Duty Factor. Null Action and active proportional-integral-derivative conditions established operational baselines. Fixed-ramp sessions were segmented to evaluate reproducibility, drift, and spill-to-spill spread, while a deliberate quadrupole-ramp step identified the beam-response peak and trough. Median profiles were reproducible within fixed conditions but drifted over multi-hour periods. The resulting baselines and response features support future physics-based and machine-learning approaches to real-time spill regulation.

Prescott, Matthew J. [Purdue U., West Lafayette; F↗

DOE OSTI2018

Integrating Natural Language Processing and Machine Learning Algorithms to Categorize Oncologic Response in Radiology Reports

A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer. We combined each of three NLP techniques with five ML algorithms to predict the assigned label using the unstructured report text and compared the performance of each combination. The three NLP algorithms included term frequency-inverse document frequency (TF-IDF), term frequency weighting (TF), and 16-bit feature hashing. The ML algorithms included logistic regression (LR), random decision forest (RDF), one-vs-all support vector machine (SVM), one-vs-all Bayes point machine (BPM), and fully connected neural network (NN). The best-performing NLP model consisted of tokenized unigrams and bigrams with TF-IDF. Increasing N-gram length yielded little to no added benefit for most ML algorithms. With all parameters optimized, SVM had the best performance on the test dataset, with 90.6 average accuracy and F score of 0.813. The interplay between ML and NLP algorithms and their effect on interpretation accuracy is complex. The best accuracy is achieved when both algorithms are optimized concurrently.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2023

Bottleneck Detection in Modular Construction Factories Using Computer Vision

The construction industry is increasingly adopting off-site and modular construction methods due to the advantages offered in terms of safety, quality, and productivity for construction projects. Despite the advantages promised by this method of construction, modular construction factories still rely on manually-intensive work, which can lead to highly variable cycle times. As a result, these factories experience bottlenecks in production that can reduce productivity and cause delays to modular integrated construction projects. To remedy this effect, computer vision-based methods have been proposed to monitor the progress of work in modular construction factories. However, these methods fail to account for changes in the appearance of the modular units during production, they are difficult to adapt to other stations and factories, and they require a significant amount of annotation effort. Due to these drawbacks, this paper proposes a computer vision-based progress monitoring method that is easy to adapt to different stations and factories and relies only on two image annotations per station. In doing so, the Scale-invariant feature transform (SIFT) method is used to identify the presence of modular units at workstations, and the Mask R-CNN deep learning-based method is used to identify active workstations. This information was synthesized using a near real-time data-driven bottleneck identification method suited for assembly lines in modular construction factories. This framework was successfully validated using 420 h of surveillance videos of a production line in a modular construction factory in the U.S., providing 96% accuracy in identifying the occupancy of the workstations and an F-1 Score of 89% in identifying the state of each station on the production line. The extracted active and inactive durations were successfully used via a data-driven bottleneck detection method to detect bottleneck stations inside a modular construction factory. The implementation of this method in factories can lead to continuous and comprehensive monitoring of the production line and prevent delays by timely identification of bottlenecks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2017

Magnetic iron oxide nanoparticles for the collection and direct measurement of adsorbed alpha-emitting radionuclides from environmental waters by liquid scintillation analysis

Radioactive contamination in the environment, be it from accidental or intentional release, can create an urgent need to assess water and food supplies, the environment, and monitor human health. Alpha-emitting radionuclides represent the most ionizing, and therefore the most damaging, form of radiation when internalized. Additionally, because of its ease of energy attenuation in solids or liquids, alpha emissions cannot be reliably monitored using non-destructive means. In the event of such an emergency, rapid and efficient methods will be needed to screen scores of samples (food, water, and human excreta) within a short time window. Unfortunately, the assay of alpha-emitting radionuclides using traditional radioanalytical methods is typically labor intensive and time consuming. The creation of analytical counting sources typically requires a series of chemical treatment steps to achieve well performing counting sources. In an effort to devise radioanalytical methods that are fast, require little labor, and minimize the use of toxic or corrosive agents, researchers at PNNL have evaluated magnetite (Fe3O4) nanoparticles as extracting agents for alpha-emitting radionuclides from chemically unmodified aqueous systems. It is demonstrated that bare magnetic nanoparticles exhibit high affinity for representative α-emitting radionuclides (241Am and 210Po) from representative aqueous matrices: river and ground water. Furthermore, use of the magnetic properties of these materials to concentrate the sorbed analyte from the bulk aqueous solution has been demonstrated. The nanoparticle concentrate can be either directly dispensed into scintillation cocktail, or first dissolved and then added to scintillation cocktail as a solution for alpha emission assay by liquid scintillation analysis. Despite the extreme quench caused by the metal oxide suspensions, the authors have demonstrated that quench correction features available on modern liquid scintillation analyzers can be employed to assure that quench-induced analytical biases can be avoided.

actinide↗