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DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗
DOE OSTI2021

Striving to translate shale physics across ten orders of magnitude: What have we learned?

Shales will play an important role in the successful transition of energy from fossil-based resources to renewables in the coming decades. Aside from being a significant source of low-carbon intensity fuels, like natural gas, they also serve as geologic seals of subsurface formations that may be used to isolate nuclear waste, sequester CO 2 , or store intermittent energy (e.g., solar hydrogen). Despite their importance, shales pose significant engineering and environmental challenges due to their nanoporous structure and extreme heterogeneity that spans at least ~10 orders of magnitude in spatial scale. Two challenges inhibit a system-level understanding: (1) the physics of fluid flow and phase behavior in shales are poorly understood due to the dominant molecular interactions between minerals and fluids under confinement, and (2) the apparent lack of scale separation that prevents a reliable (closed) description of the physics at any single scale of observation. In this review, we focus on the latter issue and discuss scale translation, which in its broadest sense is transforming data or simulations from one spatiotemporal scale to another. While effective scale translation is not exclusive to shales, but all geologic porous media, the need for it is especially acute in shales given their high degree of heterogeneity. Classical theories like homogenization, while indispensable, fail when scales are not separated. Other methods, like numerical upscaling, scale-translate in only one direction: small to large, but not the reverse, called downscaling. However, the confluence of advances in three areas are bringing challenging problems such as shales within reach: increased computational power and scalable algorithms; high-resolution imaging and multi-modal data acquisition; and machine learning to process massive amounts of data. While these advances equip geoscientists with a wide array of experimental and computational tools, no individual tool can probe the entire gamut of heterogeneity in shales. Their effective use, therefore, requires an ability to bridge between various data types obtained at different scales. The aim of this review is to present a coherent account of computational and experimental methods that may be used to achieve just that, i.e., to perform scale translation. We provide a broader definition of scale translation, one that transcends classical homogenization and upscaling methods, but is consistent with them and accommodates notions like downscaling and data translation. After a brief introduction to homogenization, we review hybrid methods, numerical upscaling and its recent extensions, multiscale computing, high-resolution imaging, and machine learning. We place particular emphasis on multiscale computing and propose an algorithmic framework to bridge between the pore (micro) and Darcy (macro) scales. Throughout the paper, we draw comparisons between the various methods and highlight their (often hidden) similarities, differences, benefits, and pitfalls. We finally conclude with two case studies on shales that exemplify some of the methods presented.

58 GEOSCIENCES↗
DOE OSTI2021

Scalable FBP decomposition for cone-beam CT reconstruction

Filtered Back-Projection (FBP) is a fundamental compute intense algorithm used in tomographic image reconstruction. Cone-Beam Computed Tomography (CBCT) devices use a cone-shaped X-ray beam, in comparison to the parallel beam used in older CT generations. Distributed image reconstruction of cone-beam datasets typically relies on dividing batches of images into different nodes. This simple input decomposition, however, introduces limits on input/output sizes and scalability.We propose a novel decomposition scheme and reconstruction algorithm for distributed FPB. This scheme enables arbitrarily large input/output sizes, eliminates the redundancy arising in the end-to-end pipeline and improves the scalability by replacing two communication collectives with only one segmented reduction. Finally, we implement the proposed decomposition scheme in a framework that is useful for all current-generation CT devices (7th gen). In our experiments using up to 1024 GPUs, our framework can construct 40963 volumes, for real-world datasets, in under 16 seconds (including I/O).

Chen, Peng↗
DOE OSTI2021

Hyperstoichiometric Uranium Dioxides: Rapid Synthesis and Irradiation-Induced Structural Changes

Uranium dioxide (UO 2 ), the primary fuel for commercial nuclear reactors, incorporates excess oxygen forming a series of hyperstoichiometric oxides. Thin layers of these oxides, such as UO 2.12 , form readily on the fuel surface and influence its properties, performance, and potentially geologic disposal. This work reports a rapid and straightforward combustion process in uranyl nitrate–glycine–water solutions to prepare UO 2.12 nanomaterials and thin films. We also report on the investigation of the structural changes induced in the material by irradiation. Despite the simple processing aspects, the combustion synthesis of UO 2.12 has a sophisticated chemical mechanism involving several exothermic steps. Raman spectroscopy and single-crystal X-ray diffraction (XRD) measurements reveal the formation of a complex compound containing the uranyl moiety, glycine, H 2 O, and NO 3 – groups in reactive solutions and dried combustion precursors. Combustion diagnostic methods, gas-phase mass spectroscopy, differential scanning calorimetry (DSC), and extracted activation energies from DSC measurements show that the rate-limiting step of the process is the reaction of ammonia with nitrogen oxides formed from the decomposition of glycine and uranyl nitrate, respectively. However, the exothermic decomposition of the complex compound determines the maximum temperature of the process. In situ transmission electron microscopy (TEM) imaging and electron diffraction measurements show that the decomposition of the complex compound directly produces UO 2 . The incorporation of oxygen at the cooling stage of the combustion process is responsible for the formation of UO 2.12 . Spin coating of the solutions and brief annealing at 670 K allow the deposition of uniform films of UO 2.12 with thicknesses up to 300 nm on an aluminum substrate. Irradiation of films with Ar 2+ ions (1.7 MeV energy, a fluence of up to 1 × 10 17 ions/cm 2 ) shows unusual defect-simulated grain growth and enhanced chemical mixing of UO 2.12 with the substrate due to the high uranium ion diffusion in films. As a result, the method described in this work allows the preparation of actinide oxide targets for fundamental nuclear science research and studies associated with stockpile stewardship.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

Genomes to Structure and Function Workshop Report 2022

The goal of the U.S. Department of Energy (DOE) Biological and Environmental Research (BER) Program is to achieve a predictive understanding of complex biological, earth, and environmental systems with the aim of advancing the nation’s energy and infrastructure security. (https://www.energy.gov/science/ ber/biological-and-environmental-research). To pursue this goal, collaborations among experts in diverse research areas that lead to multidisciplinary projects are indispensable. The roles of DOE’s User Facilities, which offer unique and powerful resources for such research projects, are evolving, and expectations for the facilities are increasing. To respond to Users’ needs, the Joint Genome Institute (JGI) and Environmental Molecular Sciences Laboratory (EMSL) initiated the Facilities Integrating Collaborations for User Science (FICUS) program in 2014. This collaboration has grown into a popular and successful program, advancing more than 100 multidisciplinary projects to date. Similarly, the new interFacility collaborations among the JGI, EMSL, and User resources for BER structural biology and imaging at the Basic Energy Science (BES) Program’s synchrotron and neutron facilities are becoming essential for cutting-edge transdisciplinary science. To further explore the need for the BER research community to combine genomic, functional, and structural approaches to advance their research, an organizing committee was formed to develop and jointly host a 3-part workshop. The committee’s members represented seven DOE National Laboratory User Facilities (Appendix 1 lists the members). The “Genomes to Structure and Function” virtual workshop (see Appendices 2–5) was composed of three sessions. The first session, titled “Molecular Structures” (October 27– 28, 2021), highlighted diverse integrative experimental and computational approaches correlating structural data with sequencing and functional information, as well as predicting protein structures to model complex biological systems. The second session, “Intracellular Organization, and Material Synthesis and Decomposition” (December 15–16, 2021), covered imaging methods for observing, quantifying, and manipulating biosystems. The third session, “Imaging the Rhizosphere and Cellular Organization” (January 26–27, 2022) emphasized advanced and non-invasive imaging techniques applied to plant root-microbe-soil interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Progress on Associate-Particle Imaging Algorithms, 2020

The present work describes progress on the development of imaging algorithms that use fast neutron signatures acquired using the associated-particle imaging (API) method. The present work complements ongoing work to develop neutron source and detector hardware to enable field inspection by investigating algorithms that are capable of discriminating between critical materials or extracting three-dimensional geometrical information from single-sided or transmission measurements. The present work is divided into three approaches:(1)Iterative reconstruction of inelastic gamma-ray emissions to perform three-dimensional time-of-flight imaging in a single view in either transmission or backscatter configurations. Iterative reconstruction enables image resolution better than the inherent TOF resolution.(2)Decomposition of registered neutron and x ray radiographs into an assumed material list for each pixel in the image.(3)Material identification using full spectral analysis that includes the emergent neutron and gamma ray energies, times, and angles.Progress for each approach is summarized for fiscal year 2020.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Close Major-merger Pairs at z = 0: Bulge-to-total Ratio and Star Formation Enhancement

We present a study of the bulge-to-total ratio (B/T) of a Ks-band-selected sample of 88 close major-merger pairs of galaxies (H-KPAIR), based on two-dimensional decompositions of SDSS r-band images with galfit. We investigate the dependence of the interaction-induced specific star formation rate enhancement (sSFR enh ) on the B/T ratio, and the effects of this dependence on the differences between star-forming galaxies (SFGs) in spiral+spiral (S+S) and spiral+elliptical (S+E) pairs. Of all the 132 spiral galaxies in H-KPAIR, the 44 in S+E pairs show higher B/T than those in the 44 S+S pairs, with means of B/T = 0.35 ± 0.05 and B/T = 0.26 ± 0.03, respectively. Furthermore, there is a strong negative dependence of sSFR enh on the B/T ratio, and only paired SFGs with B/T < 0.3 show significant (>5σ) enhancement. Paired SFGs in S+S pairs show a similar trend, and many disky SFGs (B/T < 0.1) in S+S pairs have strong sSFR enhancements (sSFR enh > 0.7 dex). For SFGs in S+E pairs, the sSFR has no clear B/T dependence, nor any significant enhancement in any B/T bin. Disky SFGs in S+S pairs show significant (>4σ) enhancement in molecular gas content (${M}_{{{\rm{H}}}_{2}}/{M}_{\mathrm{star}}$), while SFGs in S+E pairs have no such enhancement in any B/T bin. No significant enhancement of the total gas content (Mgas/Mstar) is found in any B/T bin for paired galaxies. The star formation efficiency of either the total gas (SFE gas = SFR/M gas ) or molecular gas (${\mathrm{SFE}}_{{{\rm{H}}}_{2}}=\mathrm{SFR}/{M}_{{{\rm{H}}}_{2}}$) does not depend on the B/T ratio. The only significant (> 4σ) SFE enhancement found for paired SFGs is the SFE gas for disky SFGs in S+S pairs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Tensorized Feature Spaces for Feature Explosion

In this paper 1 1 This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-000R22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan) This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-000R22725., we present a novel framework that uses tensor factorization to generate richer feature spaces for pixel classification in hyperspectral images. In particular, we assess the performance of different tensor rank decomposition methods as compared to the traditional kernel-based approaches for the hyperspectral image classification problem. We propose Orion, which takes as input a hyperspectral image tensor and a rank and outputs an enhanced feature space from the factor matrices of the decomposed tensor. Our method is a feature explosion technique that inherently maps low dimensional input space in $\mathbb{R}^{K}$ to high dimensional space in $\mathbb{R}^{R}$ , where $R\gg K$ , say in the order of 1000x, like a kernel. We show how the proposed method exploits the multi-linear structure of hyperspectral three dimensional tensor. We demonstrate the effectiveness of our method with experiments on three publicly available hyperspectral datasets with labeled pixels and compare their classification performance against traditional linear and non-linear supervised learning methods such as SVM with Linear, Polynomial, RBF kernels, and the Multi-Layer Perceptron model. Finally, we explore the relationship between the rank of the tensor decomposition and the classification accuracy using several hyperspectral datasets with ground truth.

Pasricha, Ravdeep Singh↗
DOE OSTI2024

Assessment of Accelerated Stress Testing Data for Silicon Photovoltaics Using Tensor Decomposition Methods

In this work, we examine the use of high-order tensor decompositions to analyze degradation pathways emerging from accelerated stress testing of silicon photovoltaic (PV) modules. Matrix-based decompositions are powerful tools for studying two-dimensional data arrays and form the foundation of a host of classical data analysis techniques. Tensors are high-order extrapolations of matrices that are able to account for more parameter dimensions, and a variety of tensor decomposition methods have been developed that similarly seek to extend insights from matrix decompositions to higher dimensions. Applying and interpreting tensor decomposition methods to sequences of PV module image data, we seek to uncover and isolate different degradation modes occurring from accelerated stress testing procedures. Further, we consider the contributions of different modes to PV module performance degradations.

data analysis↗
DOE OSTI2019

Cookoff experiments of a melt cast explosive (Comp-B3)

Validated models of melt cast explosives exposed to accidental fires are essential for safety analysis. In the current work, we provide several experiments that can be used to develop and validate cookoff models of melt cast explosives such as Comp-B3 composed of 60:40 wt% RDX:TNT. We present several vented and sealed experiments from 2.5 mg to 4.2 kg of Comp-B3 in several configurations. We measured pressure, spatial temperature, and ignition time. Some experiments included borescope images obtained during both vented and sealed decomposition. We observed the TNT melt, the suspension of RDX particles in the melt, bubble formation caused by RDX decomposition, and bubble-induced mixing of the suspension. The RDX suspension did not completely dissolve, even as temperatures approached ignition. Our results contrast with published measurements of RDX solubility in hot TNT that suggest RDX would be completely dissolved at these high temperatures. These different observations are attributed to sample purity. We did not observe significant movement of the two-phase mixture until decomposition gases formed bubbles. Bubble generation was inhibited in our sealed experiments and suppressed mixing.

42 ENGINEERING↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2020

Structural and stellar-population properties versus bulge types in Sloan Digital Sky Survey central galaxies

ABSTRACT This paper studies pseudo-bulges (P-bulges) and classical bulges (C-bulges) in Sloan Digital Sky Survey (SDSS) central galaxies using the new bulge indicator ΔΣ1, which measures relative central stellar-mass surface density within 1 kpc. We compare ΔΣ1 to the established bulge-type indicator Δ〈μe〉 from Gadotti (2009) and show that classifying by ΔΣ1 agrees well with Δ〈μe〉. ΔΣ1 requires no bulge–disc decomposition and can be measured on SDSS images out to z = 0.07. Bulge types using it are mapped on to 20 different structural and stellar-population properties for 12 000 SDSS central galaxies with masses 10.0 < log M*/M⊙ < 10.4. New trends emerge from this large sample. Structural parameters show fairly linear log–log relations versus ΔΣ1 and Δ〈μe〉 with only moderate scatter, while stellar-population parameters show a highly non-linear ‘elbow’ in which specific star formation rate remains roughly flat with increasing central density and then falls rapidly at the elbow, where galaxies begin to quench. P-bulges occupy the low-density end of the horizontal arm of the elbow and are universally star forming, while C-bulges occupy the elbow and the vertical branch and exhibit a wide range of star formation rates at a fixed density. The non-linear relation between central density and star formation rate has been seen before, but this mapping on to bulge class is new. The wide range of star formation rates in C-bulges helps to explain why bulge classifications using different parameters have sometimes disagreed in the past. The elbow-shaped relation between density and stellar indices suggests that central structure and stellar populations evolve at different rates as galaxies begin to quench.

Luo, Yifei↗
DOE OSTI2024

Data-Driven Compression of Electron-Phonon Interactions

First-principles calculations of electron interactions in materials have seen rapid progress in recent years, with electron-phonon ( e − ph ) interactions being a prime example. However, these techniques use large matrices encoding the interactions on dense momentum grids, which reduces computational efficiency and obscures interpretability. For e − ph interactions, existing interpolation techniques leverage locality in real space, but the high dimensionality of the data remains a bottleneck to balance cost and accuracy. Here we show an efficient way to compress e − ph interactions based on singular value decomposition (SVD), a widely used matrix and image compression technique. Leveraging (un)constrained SVD methods, we accurately predict material properties related to e − ph interactions—including charge mobility, spin relaxation times, band renormalization, and superconducting critical temperature—while using only a small fraction (1%–2%) of the interaction data. These findings unveil the hidden low-dimensional nature of e − ph interactions. Furthermore, they accelerate state-of-the-art first-principles e − ph calculations by about 2 orders of magnitude without sacrificing accuracy. Our Pareto-optimal parametrization of e − ph interactions can be readily generalized to electron-electron and electron-defect interactions, as well as to other couplings, advancing quantitative studies of condensed matter. Published by the American Physical Society 2024

Physics↗
DOE OSTI2024

Spinning metasurface stack for spectro-polarimetric thermal imaging

Spectro-polarimetric imaging in the long-wave infrared (LWIR) region plays a crucial role in applications from night vision and machine perception to trace gas sensing and thermography. However, the current generation of spectro-polarimetric LWIR imagers suffers from limitations in size, spectral resolution, and field of view (FOV). While meta-optics-based strategies for spectro-polarimetric imaging have been explored in the visible spectrum, their potential for thermal imaging remains largely unexplored. In this work, we introduce an approach for spectro-polarimetric decomposition by combining large-area stacked meta-optical devices with advanced computational imaging algorithms. The co-design of a stack of spinning dispersive metasurfaces along with compressive sensing and dictionary learning algorithms allows simultaneous spectral and polarimetric resolution without the need for bulky filter wheels or interferometers. Our spinning-metasurface-based spectro-polarimetric stack is compact (<10×10×10cm) and robust, and it offers a wide field of view (20.5°). We show that the spectral resolving power of our system substantially enhances performance in machine learning tasks such as material classification, a challenge for conventional panchromatic thermal cameras. Our approach represents a significant advance in the field of thermal imaging for a wide range of applications including heat-assisted detection and ranging (HADAR).

Optics↗
DOE OSTI2020

Bar rejuvenation in S0 galaxies?

ABSTRACT Based on the colour measurements from a multiband, multicomponent 2D decompositions of S0 and spiral galaxies using SDSS images, we found that bars are bluer in S0 galaxies compared to the spiral galaxies. Most of the S0s in our sample have stellar masses ∼L* galaxies. The environment might have played an important role as most of the S0s with bluer bars are in the intermediate-density environment. The possibility of minor mergers and tidal interactions that occurs frequently in the intermediate-density environment might have caused either a bar to form and/or induce star formation in the barred region of S0 galaxies. The underlying discs show the usual behaviour being redder in S0s compared to spiral galaxies while the bulges are red and old for both S0 and spiral galaxies. The finding of bluer bars in S0 galaxies is a puzzling issue and poses an interesting question at numerical and theoretical studies most of which shows that the bars are long-lived structures with old stellar populations.

Barway, Sudhanshu↗
DOE OSTI2026

Quiescent Host Galaxies of Extended Quasars Revealed by Spectrophotometric Decomposition

Previous works of low-redshift quasar host galaxies have focused on compact quasars and found that their host galaxies are mainly star-forming galaxies. Here, we present a study of host galaxies for quasars with extended morphologies in ground-based optical images. We select a sample of more than 1000 type 1 quasars at redshift 0.1 < z < 1 that are classified as extended objects by the Dark Energy Spectroscopic Instrument (DESI). Combining high-resolution spectra from DESI and high-quality images from Subaru Hyper Suprime-Cam, we develop a spectrophotometric decomposition technique to iteratively decompose each quasar into an active galactic nucleus (AGN) component and its host galaxy. The technique can effectively break the degeneracy between the AGN and host components and capture the host spectral features. Our results show that the host galaxies of most quasars have low star formation rates (SFRs) and low specific SFRs, indicating that they are quiescent galaxies. Many of them exhibit prominent post-starburst features with the existence of significant old stellar populations. These properties are quite different from the nature of compact quasars with star-forming host galaxies. In addition, the relation between the black hole mass and stellar mass for our sample is broadly consistent with the canonical local relations. This work is complementary to the previous studies and suggests that the host galaxies of low-redshift quasars are more diverse than was thought.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Glassy carbon formation from pyrolysis of polymeric coatings on fiber-optic sensors

Deploying fiber-optic sensors in nuclear reactors requires a detailed understanding of radiation effects on the fiber materials and the transmitted signals. Previous work has shown large wavelength shifts in the reflected spectra obtained from polymer-coated fiber-optic temperature sensors exposed to high neutron fluences. The sensor drift resulting from these wavelength shifts cannot be explained by radiation effects on fused silica glass. These shifts are hypothesized to be caused by the conversion of the polymeric fiber coating to a glassy carbon via radiolysis and/or pyrolysis and subsequent radiation-induced compaction. Here, thermal degradation of these polymeric coatings was studied to provide insight into the potential origins of the sensor drift phenomenon. Acrylate- and polyimide-coated fibers were heated under various temperatures (250–1300 °C) and environments (oxidative and inert), and the resulting coating products were characterized via mass-loss data, scanning electron microscope imaging, and Raman spectroscopy. Results suggest that the polymer decomposition product of both coating types, at least under inert conditions, is indeed a glassy carbon. Analytical models that account for radiation-induced glassy carbon coating compaction show significant compressive fiber strains and predicted wavelength shifts that agree well with experimental measurements, providing additional evidence that supports the hypothesized origins of the sensor drift.

36 MATERIALS SCIENCE↗