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DOE OSTI2021

Tractable Learning in Underexcited Power Grids

Estimating the structure of physical flow networks, such as power grids, is critical to secure delivery of energy. This article discusses statistical structure estimation in power grids in the “underexcited” regime, where a subset of internal nodes has zero injection fluctuations. Prior estimation algorithms based on nodal voltages fail for such grids as the voltage covariance matrix is not invertible. Here, we propose a novel topology learning algorithm for learning underexcited general networks. Our algorithm uses physics-informed conservation laws to first identify the zero-injection buses and their neighbors, and then estimates the remaining edges in the grid. We prove the asymptotic correctness of our algorithm for grids with nonadjacent internal zero-injection nodes. More important, we theoretically analyze our algorithm’s efficacy under noisy measurements, and determine bounds on maximum noise under which asymptotically correct recovery is guaranteed. Our approach is validated through simulations with voltage samples generated on test distribution grids with real injection data and nonlinear power flow models.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

An algebraic convolution formulation for multiple-scattering correction in small-angle neutron scattering

Multiple scattering in small-angle neutron scattering (SANS) redistributes spectral weight and distorts structural interpretation, particularly for thick or strongly scattering samples. We develop a finite-dimensional spectral desmearing framework that corrects multiple scattering without resorting to integral transforms or model-dependent extrapolation. The primary intensity is expanded in an orthonormal basis adapted to the isotropic transverse-momentum measure, under which convolution reduces to a recursive tensor contraction, allowing the Poisson-weighted multiple-scattering series to be evaluated directly in a finite-dimensional basis representation. This formulation yields a stable forward–inverse mapping between apparent and primary spectra. Numerical tests demonstrate convergence under repeated convolution and accurate recovery of the single-scattering intensity. Application to SANS measurements collected at multiple neutron facilities, including the Spallation Neutron Source, the High Flux Isotope Reactor, and the Institut Laue-Langevin, shows the quantitative reconstruction of the underlying primary spectrum across a wide range of transmission conditions, including strongly attenuating samples. Here, the method provides a stable, model-agnostic framework for multiple-scattering correction in SANS and enables consistent structural interpretation across instruments and scattering regimes.

Tung, Chi-Huan [Oak Ridge National Laboratory (ORN↗

DOE OSTI2020

Inverse methods for design of soft materials

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine learning for interpreting coherent X-ray speckle patterns

Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, in this work, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions.

36 MATERIALS SCIENCE↗

DOE OSTI2023

A TOPAS model for lens-based proton radiography

Abstract Objective. Proton Radiography can be used in conjunction with proton therapy for patient positioning, real-time estimates of stopping power, and adaptive therapy in regions with motion. The modeling capability shown here can be used to evaluate lens-based radiography as an instantaneous proton-based radiographic technique. The utilization of user-friendly Monte Carlo program TOPAS enables collaborators and other users to easily conduct medical- and therapy- based simulations of the Los Alamos Neutron Science Center (LANSCE). The resulting transport model is an open-source Monte Carlo package for simulations of proton and heavy ion therapy treatments and concurrent particle imaging. Approach. The four-quadrupole, magnetic lens system of the 800-MeV proton beamline at LANSCE is modeled in TOPAS. Several imaging and contrast objects were modelled to assess transmission at energies from 230–930 MeV and different levels of particle collimation. At different proton energies, the strength of the magnetic field was scaled according to βγ, the inverse product of particle relativistic velocity and particle momentum. Main results. Materials with high atomic number, Z, (gold, gallium, bone-equivalent) generated more contrast than materials with low-Z (water, lung-equivalent, adipose-equivalent). A 5-mrad collimator was beneficial for tissue-to-contrast agent contrast, while a 10-mrad collimator was best to distinguish between different high-Z materials. Assessment with a step-wedge phantom showed water-equivalent path length did not scale directly according to predicted values but could be mapped more accurately with calibration. Poor image quality was observed at low energies (230 MeV), but improved as proton energy increased, with sub-mm resolution at 630 MeV. Significance. Proton radiography becomes viable for shallow bone structures at 330 MeV, and for deeper structures at 630 MeV. Visibility improves with use of high-Z contrast agents. This modality may be particularly viable at carbon therapy centers with accelerators capable of delivering high energy protons and could be performed with carbon therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Phonon-Assisted Ballistic Current from First-Principles Calculations

The bulk photovoltaic effect (BPVE) refers to current generation due to illumination by light in a homogeneous bulk material lacking inversion symmetry. In addition to the intensively studied shift current, the ballistic current, which originates from asymmetric carrier generation due to scattering processes, also constitutes an important contribution to the overall kinetic model of the BPVE. In this Letter, we use a perturbative approach to derive a formula for the ballistic current resulting from the intrinsic electron-phonon scattering in a form amenable to first-principles calculation. We then implement the theory and calculate the ballistic current of the prototypical BPVE material BaTiO 3 using quantum-mechanical density functional theory. The magnitude of the ballistic current is comparable to that of the shift current, and the total spectrum (shift plus ballistic) agrees well with the experimentally measured photocurrents. Furthermore, we show that the ballistic current is sensitive to structural change, which could benefit future photovoltaic materials design.

14 SOLAR ENERGY↗

DOE OSTI2024

Machine Learning (ML) Classifier to Assist Metadata Creation

The Atmospheric Radiation Measurement (ARM) Data Center is responsible for the timely collection, archival, and curation of science data products. These products are freely available through an online data repository. Metadata creation is paramount for scientific users to find and access over seven petabytes of atmospheric science data. The hierarchical metadata structure allows users to search for information at both broad and narrow levels. This project aims to leverage 30 years’ worth of manually created metadata to enable machine predictions of broad-term classifications from narrow-term descriptions. These classification predictions would assist metadata coordinators with their term selections. This paper discusses the cleaning and preprocessing of the training data, the pipeline developed to determine the best model for this task, and the creation of an API metadata classifier for ARM measurement metadata. Our results show that the Linear Support Vector Classification (LinearSVC) algorithm, along with the Term Frequency – Inverse Document Frequency (TF-IDF) vectorizer, is well-suited for our multi-class classification task. Lengthier input training data led to better results, and artificial balancing was unnecessary for this particular use case. This predictive classifier enhances efficiency in metadata creation, as well as supports greater consistency and accuracy in metadata tagging.

Collier, Hannah [ORNL] (ORCID:0000000341284292)↗

DOE OSTI2024

Direct and Inverse Photoelectron Spectroscopy Evidence for a Revised Picture of Electronic States of Negative Polarons in n‐Doped C 60

Abstract Determining the electronic levels associated with polarons, the fundamental charge carriers in organic semiconductors, is key to understanding the charge transport properties of these materials. Recent findings challenge the traditional view of these electronic levels by highlighting the importance of intra‐molecular Coulomb interactions in polarons. Experimental evidence was previously presented for a revised model of the negative polaron in the case of the polymer semiconductor poly(NDI2OD‐T 2 ); there, the addition of an excess electron was seen to lead to the emergence of a singly occupied state within the energy gap of the undoped material and an unoccupied state above the edge of the conduction states . Here, focus is on a small‐molecule semiconductor, C 60 , and spectral evidence is provided of a similar picture for the new states appearing upon polaron formation. Specifically, direct and inverse photoemission spectroscopy is used to investigate the density of states in C 60 films n‐doped with two dimeric dopants. The Coulomb interaction energy (Hubbard U ) of the C 60 anion is experimentally determined to be ≈1.1 eV, a value that aligns closely with theoretical predictions.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Imaging the Shallow Structure of the Yucca Flat at the Source Physics Experiment Phase II Site with Horizontal-to-Vertical Spectral Ratio Inversion and a Large- N Seismic Array

The Source Physics Experiment (SPE) is a series of chemical explosions at the Nevada National Security Site (NNSS) with the goal of understanding seismic-wave generation and propagation of underground explosions. To understand explosion source physics, accurate geophysical models of the SPE site are needed. Here, we utilize a large-N seismic array deployed at the SPE phase II site to generate a shallow subsurface model of shear-wave velocity. The deployment consists of 500 geophones and covers an area of, approximately, 2.5 × 2 km. The array is located in the Yucca Flat in the northeast corner of the NNSS, Nye County, Nevada. Using ambient-noise recordings throughout the large-N seismic array, we calculate horizontal-to-vertical spectral ratios (HVSRs) across the array. We obtain 2D seismic images of shear-wave velocities across the SPE phase II site for the shallow structure of the basin. In this work the results clearly image two significant seismic impedance interfaces at ~150–500 and ~350–600 m depth. The shallower interface relates to the contrast between Quaternary alluvium and Tertiary volcanic rocks. The deeper interface relates to the contrast between Tertiary volcanic rocks and the Paleozoic bedrock. The 2D subsurface models support and extend previous understanding of the structure of the SPE phase II site. This study shows that the HVSR method in conjunction with a large-N seismic array is a quick and effective method for investigating shallow structures.

58 GEOSCIENCES↗

DOE OSTI2021

The role of electron-scale turbulence in the JET tokamak: experiments and modelling

Dedicated electron heat transport experiments have been carried out in L- and H-mode Deuterium plasmas of the JET-ILW tokamak to identify the amount of electron heat carried by electron-scale electron temperature gradient (ETG) modes. Ion cyclotron resonance heating at different positions has been used to probe the response of the electron temperature inverse gradient length R/L Te to changes in electron heat flux qe, while different amounts of neutral beam heating allowed to scan the ratio of ion to electron temperature T e /T i , which is a key parameter for the onset of ETGs. Results indicate a steepening of the normalized qe vs R/L Te curve above R/LT e ~ 8 for T e /T i ≤ 1, suggestive of the ETG onset. Ion-scale gyro-kinetic (GK) simulations match the ion heat flux and the low-R/L Te part of the qe curve, but do not reproduce such steepening at high R/L Te . Multi-scale GK simulations covering both ion and electron scales and including one impurity bundling light and heavy species indicate an ETG contribution only for R/L Te values larger than the experimental ones. Sensitivity studies of such result are difficult to achieve due to limitation in numerical resources. The quasi-linear TGLF model has been used for sensitivity studies. With the same bundled impurity as the GK multi-scale, TGLF shows the qe steepening at much larger R/L Te values than in experiment, but when using the real mix of light impurities neglecting the heavy impurities, TGLF gets closer to the experimental results. Profile simulations with TGLF including both light and heavy impurities show over-prediction of T e profiles and in some cases also of density, but good T i predictions, confirming issues with the model electron stiffness for these plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Machine learning-aided inverse design for biogas upgrading through biological CO 2 conversion

The biogas upgrading process through bioconversion of CO 2 to CH 4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH 4 % and H 2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R 2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH 4 % and H 2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.

Biogas upgrading↗

DOE OSTI2021

Influence of Polymorphs and Local Defect Structures on NMR Parameters of Graphite Fluorides

In this study, the role of local molecular structure on calculated 13 C and 19 F NMR chemical shifts for graphite fluoride materials was explored by using gauge-including projector augmented wave (GIPAW) computational methods for different periodic crystal polymorphs and density functional theory (DFT) gauge-including atomic orbital (GIAO) computational methods for individual graphite fluoride platelets, i.e., fluorinated graphene (FG). The impact of stacking sequences, d -spacing, and ring conformations on fully fluorinated graphite fluoride structures was investigated. A range of different defects including Stone–Wales, F and C vacancies, void formation, and F inversion were also evaluated using FG structures. These calculations show that distinct chemical shift signatures exist for many of these polymorphs and defects, therefore providing a basis for spectral assignment and development of models describing the mean local CF structure in disordered graphite fluoride materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Trade-offs in land-based carbon removal measures under 1.5 °C and 2 °C futures

Land-based carbon removals, specifically afforestation/reforestation and bioenergy with carbon capture and storage (BECCS), vary widely in 1.5 °C and 2 °C scenarios generated by integrated assessment models. Because underlying drivers are difficult to assess, we use a well-known integrated assessment model, GCAM, to demonstrate that land-based carbon removals are sensitive to the strength and scope of land-based mitigation policies. We find that while cumulative afforestation/reforestation and BECCS deployment are inversely related, they are both typically part of cost-effective mitigation pathways, with forestry options deployed earlier. While the CO 2 removal intensity (removal per unit land) of BECCS is typically higher than afforestation/reforestation over long time horizons, the BECCS removal intensity is sensitive to feedstock and technology choices whereas the afforestation/reforestation removal intensity is sensitive to land policy choices. Finally, we find a generally positive relationship between agricultural prices and removal effectiveness of land-based mitigation, suggesting that some trade-offs may be difficult to avoid.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imaging

Abstract The problem of phase retrieval underlies various imaging methods from astronomy to nanoscale imaging. Traditional phase retrieval methods are iterative and are therefore computationally expensive. Deep learning (DL) models have been developed to either provide learned priors or completely replace phase retrieval. However, such models require vast amounts of labeled data, which can only be obtained through simulation or performing computationally prohibitive phase retrieval on experimental datasets. Using 3D X-ray Bragg coherent diffraction imaging (BCDI) as a representative technique, we demonstrate AutoPhaseNN, a DL-based approach which learns to solve the phase problem without labeled data. By incorporating the imaging physics into the DL model during training, AutoPhaseNN learns to invert 3D BCDI data in a single shot without ever being shown real space images. Once trained, AutoPhaseNN can be effectively used in the 3D BCDI data inversion about 100× faster than iterative phase retrieval methods while providing comparable image quality.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A DECADE of dwarfs: first detection of weak lensing around spectroscopically confirmed low-mass galaxies

We present the first detection of weak gravitational lensing around spectroscopically confirmed dwarf galaxies, using the large overlap between DESI DR1 spectroscopic data and DECADE/DES weak lensing catalogs. A clean dwarf galaxy sample with well-defined redshift and stellar mass cuts enables excess surface mass density measurements in two stellar mass bins ($\log \rm{M}_*=[8.2, 9.2]~M_\odot$ and $\log \rm{M}_*=[9.2, 10.2]~M_\odot$), with signal-to-noise ratios of $5.6$ and $12.4$ respectively. This signal-to-noise drops to $4.5$ and $9.2$ respectively for measurements without applying individual inverse probability (IIP) weights, which mitigates fiber incompleteness from DESI's targeting. The measurements are robust against variations in stellar mass estimates, photometric shredding, and lensing calibration systematics. Using a simulation-based modeling framework with stellar mass function priors, we constrain the stellar mass-halo mass relation and find a satellite fraction of $\simeq 0.3$, which is higher than previous photometric studies but $1.5σ$ lower than $Λ$CDM predictions. We find that IIP weights have a significant impact on lensing measurements and can change the inferred $f_{\rm{sat}}$ by a factor of two, highlighting the need for accurate fiber incompleteness corrections for dwarf galaxy samples. Our results open a new observational window into the galaxy-halo connection at low masses, showing that future massively multiplexed spectroscopic observations and weak lensing data will enable stringent tests of galaxy formation models and $Λ$CDM predictions.

To, Chun-Hao [Chicago U., Astron. Astrophys. Ctr.;↗

DOE OSTI2023

An adjoint-based optimization method for jointly inverting heterogeneous material properties and fault slip from earthquake surface deformation data

SUMMARY Analysis of tectonic and earthquake-cycle associated deformation of the crust can provide valuable insights into the underlying deformation processes including fault slip. How those processes are expressed at the surface depends on the lateral and depth variations of rock properties. The effect of such variations is often tested by forward models based on a priori geological or geophysical information. Here, we first develop a novel technique based on an open-source finite-element computational framework to invert geodetic constraints directly for heterogeneous media properties. We focus on the elastic, coseismic problem and seek to constrain variations in shear modulus and Poisson’s ratio, proxies for the effects of lithology and/or temperature and porous flow, respectively. The corresponding nonlinear inversion is implemented using adjoint-based optimization that efficiently reduces the cost function that includes the misfit between the calculated and observed displacements and a penalty term. We then extend our theoretical and numerical framework to simultaneously infer both heterogeneous Earth’s structure and fault slip from surface deformation. Based on a range of 2-D synthetic cases, we find that both model parameters can be satisfactorily estimated for the megathrust setting-inspired test problems considered. Within limits, this is the case even in the presence of noise and if the fault geometry is not perfectly known. Our method lays the foundation for a future reassessment of the information contained in increasingly data-rich settings, for example, geodetic GNSS constraints for large earthquakes such as the 2011 Tohoku-oki M9 event, or distributed deformation along plate boundaries as constrained from InSAR.

Geochemistry & Geophysics↗

DOE OSTI2023

First Evidence of Axial Shape Asymmetry and Configuration Coexistence in Zn 74 : Suggestion for a Northern Extension of the N = 40 Island of Inversion

The excited states of N=44 74 Zn were investigated via γ-ray spectroscopy following 74 Cu β decay. By exploiting γ-γ angular correlation analysis, the 2$_2^+$, 3$_1^+$, 0$_2^+$, and 2$_3^+$ states in 74 Zn were firmly established. The γ-ray branching and E2/M1 mixing ratios for transitions deexciting the 2$_2^+$, 3$_1^+$, and 2$_3^+$ states were measured, allowing for the extraction of relative B(E2) values. In particular, the 2$_3^+$→0$_2^+$ and 2$_3^+$→4$_1^+$ transitions were observed for the first time. Here the results show excellent agreement with new microscopic large-scale shell-model calculations, and are discussed in terms of underlying shapes, as well as the role of neutron excitations across the N=40 gap. Enhanced axial shape asymmetry (triaxiality) is suggested to characterize 74 Zn in its ground state. Furthermore, an excited K=0 band with a significantly larger softness in its shape is identified. A shore of the N=40 "island of inversion" appears to manifest above Z=26, previously thought as its northern limit in the chart of the nuclides.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Understanding structure-processing relationships in metal additive manufacturing via featurization of microstructural images

Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, in this study, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) based model of the AM process is used to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a parametric study to explore the relationship between microstructural features and processing conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors; therefore, multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative in-formation that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.

36 MATERIALS SCIENCE↗