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DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗

DOE OSTI2024

AstroCLIP: a cross-modal foundation model for galaxies

ABSTRACT We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used – without any model fine-tuning – for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pre-training separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and specific-star-formation rate), we beat this supervised baseline by 19 per cent in terms of R2. We also compare our results with a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of R2, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra.

Parker, Liam (ORCID:0009000749521674)↗

DOE OSTI2025

Molecular-Level Insights into the NMR Relaxivity of Gadobutrol Using Quantum and Classical Molecular Simulations

MRI is an indispensable diagnostic tool in modern medicine; however, understanding the molecular-level processes governing NMR relaxation of water in the presence of MRI contrast agents remains a challenge, hindering the molecular-guided development of more effective contrast agents. By using quantum-based polarizable force fields, the first-of-its-kind molecular dynamics (MD) simulations of Gadobutrol are reported where the 1 H NMR longitudinal relaxivity r 1 of the aqueous phase is determined without any adjustable parameters. The MD simulations of r 1 dispersion (i.e., frequency dependence) show good agreement with measurements at frequencies of interest in clinical MRI. Importantly, the simulations reveal key insights into the molecular level processes leading to r 1 dispersion by decomposing the NMR dipole–dipole autocorrelation function G(t) into a discrete set of molecular modes, analogous to the eigenmodes of a quantum harmonic oscillator. The molecular modes reveal important aspects of the underlying mechanisms governing r 1 , such as its multiexponential nature and the importance of the second eigenmodal decay. By simply analyzing the MD trajectories on a parameter-free approach, the Gadobutrol simulations show that the outer-shell water contributes ∼50% of the total relaxivity r 1 compared to the inner-shell water, in contrast to simulations of (nonchelated) gadolinium-aqua where the outer shell contributes only ∼15% of r 1 . The deviation between simulations and measurements of r 1 below clinical MRI frequencies is used to determine the low-frequency electron-spin relaxation time for Gadobutrol, in good agreement with independent studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Neutron Grating Interferometry at the High Flux Isotope Reactor

Neutron imaging is a non-destructive probe that can spatially resolve bulk materials’ internal features/structures. Neutron imaging spatial resolution is driven by detector technology, i.e., pixel size, and is currently on the order of 10–20 µm. This limitation can be alleviated by the use of neutron grating interferometry – an advanced neutron imaging technique – that enables simultaneous access to three contrast mechanisms: attenuation, differential phase, and small-angle neutron scattering. Recently, a Talbot-Lau neutron grating interferometry has been implemented at the Multimodal Advanced Radiography Station at the High Flux Isotope Reactor. In this work, the design, setup and performance of the grating apparatus are presented.

Zhang, Yuxuan [ORNL] (ORCID:0000000200831408)↗

DOE OSTI2024

Quantum ghost imaging microscopy depth-of-field study

Quantum ghost imaging approaches have been proposed to enhance biological microscopy, for example, using 2D visible detectors to provide IR images or providing additional dimensions of spatial or spectral information. Toward the goal of making such imaging schemes practical, we compare image quality and depth-of-field between traditional images and ghost images at the same excitation levels. We measure how image quality and depth-of-field depend on the parameters of the entangled light produced using type-I spontaneous parametric down-conversion (SPDC). We use a pair of time-synchronized, photon-timing single-photon avalanche diode (SPAD) array detectors to capture two distinct microscope imaging paths simultaneously on a photon-pair-by-photon-pair basis: one in a traditional imaging pathway and the other a quantum ghost imaging pathway. We calculate the depth-of-field, resolution, contrast, and signal-to-noise ratio (SNR) through the parameter space of a β-Barium Borate (BBO) type-I bulk non-linear crystal length and angle. Our results provide a basis for choosing parameters for quantum ghost imaging with type-I SPDC sources.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Multidimensional images and aberrations in STEM

Recent advances in scanning transmission electron microscopy (STEM) have led to increased development of multi-dimensional STEM imaging modalities and novel image reconstruction methods. This interest arises because the main electron lens in a modern transmission electron microscope usually has a diffraction-space information limit that is significantly better than the real-space resolution of the same lens. This state-of-affairs is sometimes shared by other scattering methods in modern physics and contributes to a broader excitement surrounding multidimensional techniques that scan a probe while recording diffraction-space images, such as ptychography and scanning nano-beam diffraction. However, the contrasting resolution in the two spaces raises the question as to what is limiting their effective performance. Here, we examine this paradox by considering the effects of aberrations in both image and diffraction planes, and likewise separate the contributions of pre- and post-sample aberrations. In conclusion, this consideration provides insight into aberration-measurement techniques and might also indicate improvements for super-resolution techniques.

Hoglund, Eric R.↗

DOE OSTI2024

Electron microscopy evidence of gadolinium toxicity being mediated through cytoplasmic membrane dysregulation

Past functional toxicogenomic studies have indicated that genes relevant to membrane lipid synthesis are important for tolerance to the lanthanides. Moreover, previously reported imaging of patient's brains following administration of gadolinium-based contrast agents shows gadolinium lining the vessels of the brain. Taken together, these findings suggest the disruption of cytoplasmic membrane integrity as a mechanism by which lanthanides induce cytotoxicity. In the presented work we used scanning transmission electron microscopy and spatially resolved elemental spectroscopy to image the morphology and composition of gadolinium, europium, and samarium precipitates that formed on the outside of yeast cell membranes. In no sample did we find that the lanthanide contaminant had crossed the cell membrane, even in experiments using yeast mutants with disrupted genes for sphingolipid synthesis—the primary lipids found in yeast cytoplasmic membranes. Rather, we have evidence that lanthanides are co-located with phosphorus outside the yeast cells. Finally, these results lead us to hypothesize that the lanthanides scavenge or otherwise form complexes with phosphorus from the sphingophospholipid head groups in the cellular membrane, thereby compromising the structure or function of the membrane, and gaining the ability to disrupt membrane function without entering the cell.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Two-dimensional burn velocity and analysis of burn products of nitromethane at high pressure

We have studied the laser-initiated deflagration of nitromethane (CH 3 NO 2 ) under high static pressure in the diamond anvil cell. Time-resolved images of the deflagrations have been obtained using intensified CCDs (ICCD). In contrast with previous work, we rely on spontaneous emission from the reaction, rather than changes in the speckle pattern produced by artificial illumination of the sample. Furthermore, as opposed to the 1D records obtained previously with streak cameras, ICCDs permit imaging of the burn in two dimensions providing the ability to directly observe anisotropic deflagration behavior. Here, we report several examples of this behavior and discuss its possible origins. We have also investigated the products of the reaction using Raman spectroscopy. At pressures below 25 GPa, the burn product is observed to be opaque and has a Raman spectrum consistent with a carbonaceous soot. At pressures above 25 GPa, the burn product is observed to be transparent (consistent with earlier reports) and its Raman spectrum reveals the presence of molecular N 2 and a nitrogen-carbonate species. The latter species can be recovered to ambient pressure and has a Raman spectrum consistent with ammonium carbonate or ammonium bicarbonate. To aid interpretation of the Raman spectra, measurements have also been made on the isotopologues CH 3 15 NO 2 and 13 CH 3 NO 2 . This work establishes the importance of advanced time-resolved imaging to reveal the details of deflagration under high pressure and further advances our understanding of the corresponding chemistry.

Deflagration↗

DOE OSTI2024

Uncovering the three-dimensional structure of upconverting core–shell nanoparticles with multislice electron ptychography

In photon upconverting core–shell nanoparticles, structure strongly dictates performance. Typical imaging in scanning transmission electron microscopy has sufficient resolution to probe the atomic structure of these nanoparticles, but contrast, dose, and projection limitations make conventional methods insufficient for fully characterizing these structures. Phase retrieval techniques provide a promising alternative imaging mode, and, in particular, multislice electron ptychography can recover depth-dependent information. Here, we study beam-sensitive photon upconverting core–shell nanoparticles with a multislice ptychography approach using a low electron dose to avoid damage. Large strain fields arise in these heterostructures due to the mismatch in lattice parameter between the core and the shell. We reconstruct both a nanoparticle that appears defect-free and one that has a large break in the side and map the distribution of strain in 3D by computing distortion fields from high-resolution potential images of each slice. In the defect-free nanoparticle, we observe twisting of the shell, while in the broken nanoparticle, we measure the 3D position of the crack, the core, and dislocations. These results highlight the advantage of multislice electron ptychography to recover 3D information from a single scan, even under strict electron dose requirements from beam-sensitive samples.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2024

Unraveling the Nature of Lasing Emission from Hybrid Silicon Nitride and Colloidal Nanocrystal Photonic Crystals with Low Refractive Index Contrast

Silicon nitride is used for its low optical loss and high thermal stability, making it a suitable platform for visible-light applications in integrated photonic devices. However, its application has been limited due to inefficient light emission, a problem addressed by integrating various types of light emitters onto the platform. In particular, the integration of solution-processable colloidal nanocrystals (NCs) as optical gain materials onto the silicon nitride platform is a promising route but requires a more solid theoretical footing. By leveraging 2D surface-emitting photonic crystal structures combined with NCs, we effectively confine and manipulate light to achieve lasing from green to red. Building on this, we model the light–matter interactions of the low index contrast NC/nitride platform, validated by extensive experimental validations through Fourier imaging techniques, revealing the full photonic band structure and showing clear mode congestion. Finally, these comprehensive studies confirm the potential of hybrid NC-based structures for fully integrated on-chip laser applications and indicate routes for further improvement.

BIC Lasing↗

DOE OSTI2025

Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model

The thickness of 2D materials not only plays a crucial role in determining the performance of nanoelectronic and optoelectronic devices but also introduces complexities in predicting volume-dependent properties, such as energy storage capacity, due to the intrinsic vacuum within these materials. Although a plethora of experimental techniques, including but not limited to optical contrast, Raman spectroscopy, nonlinear optical spectroscopy, near-field optical imaging, and hyperspectral imaging, facilitate the measurement of 2D material thickness, comprehensive data for many materials remain elusive. Over the past decade, the exponential proliferation of 2D materials and their heterostructures has outstripped the capabilities of conventional experimental and computational approaches. In this evolving landscape, machine learning (ML) has emerged as an indispensable tool, offering a scalable approach to augment these traditional methodologies. Addressing the critical gap, we introduce THICK2D—Thickness Hierarchy Inference and Calculation Kit for 2D Materials. This Python-based computational framework harnesses an autogenerated thickness database, developed using large language models, and advanced ML algorithms to facilitate the rapid and scalable estimation of material thickness, relying solely on crystallographic data. To demonstrate the utility and robustness of THICK2D, we successfully used the toolkit to predict the thickness of more than 8000 2D-based materials, sourced from two extensive 2D materials databases. THICK2D is disseminated as an open-source utility, accessible on GitHub at https://github.com/gmp007/THICK2D, and archived on Zenodo at https://10.5281/zenodo.11216648.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗

DOE OSTI2025

Smartphone application for visualizing building air leakage

Annually, unwanted air leakage through building envelopes accounts for 4 quads of energy consumption in the United States, which translates to about 10% of total building energy consumption. Locating and sealing leakage sites is crucial fordecreasing building energy consumption. Smartphones are ubiquitous and contain sophisticated cameras and highperformance processors that could be employed to visualize air leakage using the background-oriented schlieren imaging technique, making leak detection cheaper and easier. This technique requires a textured and high-contrast background such as a brick or concrete masonry unit wall, a building air leak that has a temperature difference compared with the ambient air, and an imaging system. This work focuses on using smartphones as the imaging system to visualize air leakages. The paper discusses application development and the results of testing to determine leakage visualization performance as a function of leak temperature. The results show that leaks with a temperature difference greater than 16°C compared with the ambient air temperature were visualized using existing smartphones.

Boudreaux, Philip [ORNL] (ORCID:0000000229564665)↗

DOE OSTI2024

Reconstruction of the 4D beam matrix

The widely used transverse parameters characterizing particle beams are the Twiss parameters. These parameters can be measured experimentally but they do not fully characterize the beam since they do not account for possible correlations in particle distribution between two transverse coordinates. These correlations may occur due to uncompensated magnetic field at the cathode or misalignment of focusing quadrupoles in the transport beamline. We test a novel diagnostic for diagnosing full 4D beam matrix which may be used to identify such imperfections. The diagnostic is based on transporting the beam through the beamline which includes a quadrupole and a skew quadrupole magnets and measuring the resulting 2D beam distribution at the screen downstream. Such a measurement can be viewed as measuring a 2D projection of the 4D distribution. Different settings of the quads provide measurements of different slices of the phase space. The reconstruction of the original beam matrix from a number of measurements is done using machine learning algorithm, which provides a fast and reliable way of reconstruction for an arbitrary configuration of the scanning beamline. In August 2024, we set up the diagnostic beamline to perform a quadrupole scan of the beam. The setup includes a skew quadrupole, a regular quadrupole, and a screen. The images on the screen were post-processed to remove experimental artifacts and enhance contrast by eliminating background noise outside the core of the distribution=. The rms parameters of the distribution were then calculated and used as inputs for the reconstruction algorithm. This algorithm attempts to determine the initial beam matrix that produces expected images on the screen closely matching the observed images across all quadrupole settings. The algorithm found a solution in which the expected rms parameters closely align with the observations. Validation of the results is planned for FY25.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Source shape estimation for neutron imaging systems using convolutional neural networks

Neutron imaging systems are important diagnostic tools for characterizing the physics of inertial confinement fusion reactions at the National Ignition Facility (NIF). In particular, neutron images give diagnostic information on the size, symmetry, and shape of the fusion hot spot and surrounding cold fuel. Images are formed via collection of neutron flux from the source using a system of aperture arrays and scintillator-based detectors. Currently, reconstruction of fusion source geometry from the collected neutron images is accomplished by solving a computationally intensive maximum likelihood estimation problem via expectation maximization. In contrast, it is often useful to have simple representations of the overall source geometry that can be computed quickly. In this work, we develop convolutional neural networks (CNNs) to reconstruct the outer contours of simple source geometries. We compare the performance of the CNN for penumbral and pinhole data and provide experimental demonstrations of our methods on both non-noisy and noisy data.

Machine learning, neutron imaging, source reconstr↗

DOE OSTI2024

Shadow of a laser beam

Light, being massless, casts no shadow; under ordinary circumstances, photons pass right through each other unimpeded. Here, we demonstrate a laser beam acting like an object — the beam casts a shadow upon a surface when the beam is illuminated by another light source. We observe a regular shadow in the sense it can be seen by the naked eye, it follows the contours of the surface it falls on, and it follows the position and shape of the object (the laser beam). Specifically, we use a nonlinear optical process involving four atomic levels of ruby. We are able to control the intensity of a transmitted laser beam by applying another perpendicular laser beam. We experimentally measure the dependence of the contrast of the shadow on the power of the laser beam, finding a maximum of approximately 22%, similar to that of a shadow of a tree on a sunny day. We provide a theoretical model that predicts the contrast of the shadow. This work opens new possibilities for fabrication, imaging, and illumination.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Evaluation of sucrose crystallization kinetics using confocal microscopy: Determining differences in sucrose crystallization kinetics between single and polycrystal studies

Sucrose solution crystal growth kinetics are dependent on temperature, concentration, and impurities. A double polarized confocal microscopy system was used to obtain photographs with high contrast between growing crystals and the background for color background removal image processing. Seeded solutions of 72 wt% sucrose at 27, 35, 45, and 55°C were compared for single and polycrystals. Polycrystal analysis was determined to offer more rapid image processing and to be more applicable to systems in real operating conditions, while single-crystal analysis was determined to give more comprehensive descriptions of individual crystal behavior at the cost of slower processing.

36 MATERIALS SCIENCE↗

DOE OSTI2025

NEUWAVE-12 at ESS: Workshop Series on Wavelength Dependent Neutron Imaging Back in Full Swing

The NEUWAVE workshop series, a cornerstone for advancements in energy- or wavelength-resolved neutron imaging, held its 12th meeting at the European Spallation Source (ESS) in Lund from September 1 to 5, 2024. The participants can be seen in Figure 1 during the site visit. The series focuses on developments in techniques using phenomena such as Bragg-edges, incoherent and inelastic scattering, or neutron absorption resonances as contrast mechanisms and is therefore far more specialized than more general conferences for neutron imaging such as the WCNR or ITMNR series [Citation1]. After a break during the pandemic and record attendance at NEUWAVE-11 in Tokyo in 2023, which underscored the immense interest in wavelength dependent neutron imaging, NEUWAVE returned to its roots with a focus on a smaller group of specialists meeting in a workshop format, allowing for in-depth discussions and the exchange of new ideas.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Ultrafast nano-imaging and nano-spectroscopy

Ultrafast pump–probe nano-imaging combines scanning probe-based optical near-field microscopy with ultrafast spectroscopy to enable imaging with deep sub-wavelength spatial resolution, femtosecond temporal resolution and simultaneous spectral resolution. Ultrafast nano-imaging has gained increased attention for its ability to provide far-from-equilibrium excitation and excited-state contrast. With coherent and nonlinear probing, coupled electron, spin and lattice dynamics on elementary timescale and length scale can be resolved. Through nano-movies, ultrafast nano-imaging visualizes correlated quantum dynamics underlying the properties of solid-state materials, semiconductors, molecular electronic, photonic, photovoltaic and other functional materials. With nanometre spatial resolution, this method probes elementary dynamic processes across multiple length scales that are otherwise obscured in conventional ultrafast spectroscopy in which heterogeneities are spatially averaged. Furthermore, this Primer describes the theoretical background and experimental implementation of ultrafast nano-imaging; signal interpretation and modelling; representative examples and a perspective for the future development of the field.

Microscopy↗