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DOE OSTI2025

ScaWL: Scaling k-WL (Weisfeiler-Lehman) Algorithms in Memory and Performance on Shared and Distributed-Memory Systems

The k-dimensional Weisfeiler-Lehman (k-WL) algorithm—developed as an efficient heuristic for testing if two graphs are isomorphic—is a fundamental kernel for node embedding in the emerging field of graph neural networks. Unfortunately, the k-WL algorithm has exponential storage requirements, limiting the size of graphs that can be handled. This work presents a novel k-WL scheme with a storage requirement orders of magnitude lower while maintaining the same accuracy as the original k-WL algorithm. Due to the reduced storage requirement, our scheme allows for processing much bigger graphs than previously possible on a single compute node. For even bigger graphs, we provide the first distributed-memory implementation. Our k-WL scheme also has significantly reduced communication volume and offers high scalability. Our experimental results demonstrate that our approach is significantly faster and has superior scalability compared to five other implementations employing state-of-the-art techniques.

algorithims↗

DOE OSTI2026

Understanding the Thermal Physics and Metallurgy of Metal Big Area Additive Manufacturing

The research goal of this EPSCoR-DOE partnership is to mitigate defects in parts made using a new type of additive manufacturing (AM) process called metal Big Area Additive Manufacturing (m-BAAM). To realize this goal, the PIs will detect and correct defects in the part as it is being printed by combining fundamental knowledge of the thermal physics and metallurgy of m-BAAM with in-process sensor data. Developed at the DOE-funded Manufacturing Demonstration Facility at Oak Ridge National Laboratory, the m-BAAM process involves one or more robots working together to produce a part by fusing metal wire layer-by-layer using arc welding. The process can print large metal parts such as turbine blades, which is not possible using other AM processes. In addition, m-BAAM production rates are more than ten times faster than other AM processes while requiring one-tenth of the material cost. Despite their potential to become a critical force multiplier in the energy generation industry, m-BAAM parts may fail to print accurately due to retention of heat and uneven cooling. Overheating and anomalous cooling rates in turn can cause inconsistencies in the microstructure, leading to sudden failure when used in safety-critical applications. In other words, flaw formation in m-BAAM parts is governed by the thermal history – intensity and spatial distribution of heat inside the part during printing. The thermal history is a complex function of the part shape and process settings such as welding energy, path taken by the welding torch for deposition (tool path), wire feed rate, among others.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Machine learning pipeline for denoising low signal-to-noise ratio and out-of-distribution transmission electron microscopy datasets

High-resolution transmission electron microscopy (HRTEM) is crucial for observing material’s structural and morphological evolution at Angstrom scales, but the electron beam can alter these processes. Devices such as CMOS-based direct-electron detectors operating in electron-counting mode can be utilized to substantially reduce the electron dosage. However, the resulting images often lead to a low signal-to-noise ratio, which requires frame integration that sacrifices temporal resolution. Several machine learning (ML) models have been recently developed to successfully denoise HRTEM images. Yet, these models are often computationally expensive, and their inference speeds on GPUs are outpaced by the imaging speed of advanced detectors, precluding in situ analysis. Furthermore, the performance of these denoising models on datasets with imaging conditions that deviate from the training datasets has not been evaluated. To mitigate these gaps, we propose a new self-supervised ML denoising pipeline specifically designed for time-series HRTEM images. This pipeline integrates a blind-spot convolution neural network with pre-processing and post-processing steps, including drift correction and low-pass filtering. Results demonstrate that our model outperforms various other ML and non-ML denoising methods in noise reduction and contrast enhancement, leading to improved visual clarity of atomic features. Additionally, the model is drastically faster than U-Net-based ML models and demonstrates excellent out-of-distribution generalization. The model’s computational inference speed is in the order of milliseconds per image, rendering it suitable for application in in-situ HRTEM experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Model validation and error attribution for a drifting qubit

Qubit performance is often reported in terms of a variety of single-value metrics, each providing a facet of the underlying noise mechanism limiting performance. However, the value of these metrics may drift over long timescales, and reporting a single number for qubit performance fails to account for the low-frequency noise processes that give rise to this drift. Here, in this work, we demonstrate how we can use the distribution of these values to validate or invalidate candidate noise models. We focus on the case of randomized benchmarking (RB), where typically a single error rate is reported but this error rate can drift over time when multiple passes of RB are performed. We show that using a statistical test as simple as the Kolmogorov-Smirnov statistic on the distribution of RB error rates can be used to rule out noise models, assuming the experiment is performed over a long enough time interval to capture relevant low frequency noise. With confidence in a noise model, we show how care must be exercised when performing error attribution using the distribution of drifting RB error rate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Distribution of nickel(II) ions adsorbed at the muscovite mica (001)-water interface determined by in-situ resonant anomalous X-ray reflectivity

Mineral-water interfaces mediate adsorption, ion exchange, and secondary mineral formation that control element mobility in natural and engineered systems. Reliable prediction and control of these processes require a fundamental understanding of the interfacial structure that links adsorbed ion speciation to macroscopic sorption capacity and strength. Here, we determine atomic-scale changes in hydration and distribution of Ni(II) at the muscovite mica (001)-water interface using in situ high-resolution X-ray reflectivity (XR) and resonant anomalous X-ray reflectivity (RAXR) at 1 mM NiCl2 and pH 5.7. XR reveals reorganization of the primary hydration structure relative to that in deionized water: the water layer adsorbed in the cavity sites at a height of ~1.3 Å disappears, while distinct solution layers emerge at ~2.3, ~4.1, and ~5.6 Å above the basal oxygen plane. RAXR resolves three interfacial Ni(II) species: a dominant outer-sphere complex at 3.65 Å (~80% of the total coverage), a minor inner-sphere complex at 0.75 Å, and a low-coverage, more distant outer-sphere species at 5.63 Å. These three adsorbed Ni(II) species account for a total Ni(II) coverage of 0.54 ± 0.02 ion per unit cell area that compensates for the surface charge. These results highlight the role of interfacial hydration in controlling the speciation and stability of adsorbate cations on the negatively charged mica surface, providing quantitative insight into predicting the geochemical behavior of divalent metal cations in the aqueous environments.

Lee, Sang Soo↗

DOE OSTI2025

Tracing the Cosmic Evolution of the Cool Circumgalactic Medium of Luminous Red Galaxies with DESI Year 1 Data

We investigate the properties of the cool circumgalactic medium (CGM) of massive galaxies and their cosmic evolution. By using the year 1 dataset of luminous red galaxies (LRGs) and QSOs from the Dark Energy Spectroscopic Instrument survey, we construct a sample of approximately 600,000 galaxy-quasar pairs and measure the radial distribution and kinematics of the cool gas traced by Mg II absorption lines as a function of galaxy properties from redshift 0.4 to redshift 1.2. Our results show that the covering fraction of the cool gas around LRGs increases with redshift, following a trend similar to the global evolution of galaxy star formation rate. At small radii (< 0.3rvir), the covering fraction anti-correlates with stellar mass, suggesting that mass-dependent processes suppress the cool gas content in the inner region. In addition, we measure the gas dispersion by modeling the velocity distribution of absorbers with a narrow and a broad components -- sigma_n ~ 160 and sigma_b ~ 380 km/s -- and quantify their relative contributions. The results show that the broad component becomes more prominent in the outer region, and its relative importance in the central region grows with increasing stellar mass. Finally, we discuss possible origins of the cool gas around massive galaxies, including the contribution of satellite galaxies and the precipitation scenario.

Chang, Yu-Ling [Taiwan, Natl. Taiwan U.] (ORCID:00↗

DOE OSTI2024

Thinking Bayesian for plasma physicists

Bayesian statistics offers a powerful technique for plasma physicists to infer knowledge from the heterogeneous data types encountered. To explain this power, a simple example, Gaussian Process Regression, and the application of Bayesian statistics to inverse problems are explained. The likelihood is the key distribution because it contains the data model, or theoretic predictions, of the desired quantities. By using prior knowledge, the distribution of the inferred quantities of interest based on the data given can be inferred. Because it is a distribution of inferred quantities given the data and not a single prediction, uncertainty quantification is a natural consequence of Bayesian statistics. The benefits of machine learning in developing surrogate models for solving inverse problems are discussed, as well as progress in quantitatively understanding the errors that such a model introduces.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

The Poisson tensor completion parametric estimator

We introduce the Poisson tensor completion (PTC) estimator that exploits inter-sample relationships to compute a low-rank Poisson tensor decomposition of the frequency histogram for samples of a multivariate distribution. Our crucial observation is that the histogram bins are an instance of a space partitioning of counts and thus can be identified with a spatial non-homogeneous Poisson process. The Poisson tensor decomposition leads to a completion of the mean measure over all bins—including those containing few to no samples—and leads to our proposed estimator. A Poisson tensor decomposition models the underlying distribution of the count data and guarantees non-negative estimated values obviating the need for additional constraints to ensure non-negativity. Furthermore, we demonstrate that our PTC estimator is a substantial improvement over standard histogram-based estimators for sub-Gaussian probability distributions because of the concentration of norm phenomenon.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Community Solar and Community Solar+Storage: A Roadmap of Barriers and Solutions for Commercial Systems in NYC

Sustainable CUNY worked with decision makers and subject matter experts (SME's) to identify the barriers to and solutions for advancing commercial Community Solar (CS) and CS+Storage (CS+S) in urban areas. This roadmap captures the key challenges and solutions identified by New York City (NYC) stakeholders, including the Real Estate Board of New York (REBNY), through a collaborative process. Solar, as well as storage, are among the fastest growing energy segments in the United States, with CS, also known as Community Distributed Generation (CDG), gaining popularity with those who may not own or have access to a viable roof. Urban areas like NYC, which have a large population of renters, are particularly well suited for CS projects where credits from the power produced by a large remote installation are offered on a subscription basis to residents or businesses in the community. However, CS and CS+S projects have stalled at the doorstep of many cities. Host site owners, particularly those with large rooftops, have been slow to commit to installing CS due to competing rooftop usage and programs, limited knowledge about incentives, lack of economic data, and a complicated implementation process.

14 SOLAR ENERGY↗

DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multicolor Inks of Black Phosphorus for Midwave‐Infrared Optoelectronics

Abstract Black phosphorus (bP) based ink with a bulk bandgap of 0.33 eV ( λ = 3.7 µm) has recently been shown to be promising for large‐area, high performance mid‐wave infrared (MWIR) optoelectronics. However, the development of multicolor bP inks expanding across the MWIR wavelength range has been challenging. Here a multicolor ink process based on bP with spectral emission tuned from 0.28 eV ( λ = 4.4 µm) to 0.8 eV ( λ = 1.5 µm) is demonstrated. Specifically, through the reduction of bP particle size distribution (i.e., lateral dimension and thickness), the optical bandgap systematically blueshifts, reaching up to 0.8 eV. Conversely, alloying bP with arsenic (bP 1− x As x ) induces a redshift in the bandgap to 0.28 eV. The ink processed films are passivated with an infrared‐transparent epoxy for stable infrared emission in ambient air. Utilizing these multicolor bP‐based inks as an infrared light source, a gas sensing system is demonstrated that selectively detects gases, such as CO 2 and CH 4 whose absorption band varies around 4.3 and 3.3 µm, respectively. The presented ink formulation sets the stage for the advancement of multiplex MWIR optoelectronics, including spectrometers and spectral imaging using a low‐cost material processing platform.

Kim, Jae Ik↗

DOE OSTI2025

Robust error calibration for serial crystallography

Serial crystallography is an important technique with unique abilities to resolve enzymatic transition states, minimize radiation damage to sensitive metalloenzymes and perform de novo structure determination from micrometre-sized crystals. This technique requires the merging of data from thousands of crystals, making manual identification of errant crystals unfeasible. cctbx.xfel.merge uses filtering to remove problematic data. However, this process is imperfect, and data reduction must be robust to outliers. We add robustness to cctbx.xfel.merge at the step of uncertainty determination for reflection intensities. This step is a critical point for robustness because it is the first step where the data sets are considered as a whole, as opposed to individual lattices. Robustness is conferred by reformulating the error-calibration procedure to have fewer and less stringent statistical assumptions and incorporating the ability to down-weight low-quality lattices. We then apply this method to five macromolecular XFEL data sets and observe the improvements to each. The appropriateness of the intensity uncertainties is demonstrated through internal consistency. This is performed through theoretical CC 1/2 and I /σ relationships and by weighted second moments, which use Wilson's prior to connect intensity uncertainties with their expected distribution. This work presents new mathematical tools to analyze intensity statistics and demonstrates their effectiveness through the often underappreciated process of uncertainty analysis.

Mittan-Moreau, David W.↗

DOE OSTI2025

Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions (Final Technical Report)

The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Design strategies for ion-sieving charge mosaic membranes toward sustainable lithium extraction

Membrane-based technologies are essential for realizing sustainable ion-sieving processes such as lithium extraction. Much effort was devoted to designing new membrane materials, whereas the effect of charge distribution has been largely overlooked. Here, we filled this knowledge gap by providing a quantitative transport model for selective ion diffusion through a charge mosaic membrane (CMM). Composed of alternating regions of Li-selective ceramic and anion-selective polymeric materials, CMMs offered the unique advantage of promoting the transport of both Li + and anions while blocking other cations. As a result, the membrane achieved a high Li/Mg selectivity of 62 and a high permeation rate of 59 mmol·m −2 ·h −1 at a low feeding concentration of 30 mM, without any external driving force. Systematical experiments revealed the influence of brine Mg/Li ratio and membrane ceramic/polymer ratio on the overall extraction rate, which was consistent with the prediction of our transport model. In conclusion, the model developed in this work not only presented the design strategies of CMMs for Li extraction but also provided guidance for the development of other ion-sieving processes in general.

25 ENERGY STORAGE↗

DOE OSTI2024

A collision operator for describing dissipation in noncanonical phase space

The phase space of a noncanonical Hamiltonian system is partially inaccessible due to dynamical constraints (Casimir invariants) arising from the kernel of the Poisson tensor. When an ensemble of noncanonical Hamiltonian systems is allowed to interact, dissipative processes eventually break the phase space constraints, resulting in a thermodynamic equilibrium described by a Maxwell–Boltzmann distribution. However, the time scale required to reach Maxwell–Boltzmann statistics is often much longer than the time scale over which a given system achieves a state of thermal equilibrium. Examples include diffusion in rigid mechanical systems, as well as collisionless relaxation in magnetized plasmas and stellar systems, where the interval between binary Coulomb or gravitational collisions can be longer than the time scale over which stable structures are self-organized. Here, we focus on self-organizing phenomena over spacetime scales such that particle interactions respect the noncanonical Hamiltonian structure, but yet act to create a state of thermodynamic equilibrium. We derive a collision operator for general noncanonical Hamiltonian systems, applicable to fast, localized interactions. This collision operator depends on the interaction exchanged by colliding particles and on the Poisson tensor encoding the noncanonical phase space structure, is consistent with entropy growth and conservation of particle number and energy, preserves the interior Casimir invariants, reduces to the Landau collision operator in the limit of grazing binary Coulomb collisions in canonical phase space, and exhibits a metriplectic structure. We further show how thermodynamic equilibria depart from Maxwell–Boltzmann statistics due to the noncanonical phase space structure, and how self-organization and collisionless relaxation in magnetized plasmas and stellar systems can be described through the derived collision operator.

Boltzmann equation↗

DOE OSTI2025

Upcycling of aluminum Twitch scrap via Shear Assisted Processing and Extrusion (ShAPE)

Aluminum is publicly perceived as recyclable, but mixed alloys and impurities in aluminum scrap require dilution via energy-intensive primary aluminum or downcycling to low-quality castings. In this study, cast billets of shredded aluminum scrap (Twitch) were blended with pre-consumer AA 6061, extruded into tubes via Shear Assisted Processing and Extrusion (ShAPE), and aged to T1 and T6 tempers. Microscopy reveals that ShAPE refined and distributed the deleterious AlFeSi phases. Furthermore, the Twitch extrusions had tensile properties comparable to AA 6061 yet without homogenizing or adding primary aluminum. Energy savings were 85% compared to conventional extrusion of primary aluminum alloys.

ShAPE↗

DOE OSTI2025

Spatial Glycomics and Kidney Disease

Glycans are critical for the kidney's physiological and pathological cellular functions, and our ability to reveal their spatial distributions within tissues has helped us reveal how these carbohydrate moieties are involved in many of these processes. This review discusses the role of different types of glycans in kidney biology and disease, common approaches used for glycan imaging, and how glycan imaging has helped us better understand kidney pathology. Here, we mainly focus on emerging methods using mass spectrometry imaging (MSI) because this technology is untargeted and provides complete information on glycan composition compared to the other methods, such as lectin and metabolite labeling, which are targeted and often inform only on the specific part of a glycan structure. We especially focus on protein N-glycosylation, as this is one of the most common post-translational modifications, and these moieties play a vital role in renal structure and function. The recent advancements in MSI of N-glycans we reviewed have provided new insights into the pathophysiology of the kidney and paved the way for clinical application.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

New directions and principles for solvent extraction for recovery of lithium from aqueous brines and mineral leachates: A brief review

Increasing demand for lithium for manufacturing of batteries is fueling the unprecedented search for improved recovery and alternative sources. Wider source distribution, lower energy consumption, and greater sustainability make extraction of lithium from brines, both natural and process-derived, an attractive alternative to mineral ores. Solvent extraction, used industrially for production of metals, salts, and pharmaceuticals, has been investigated as a methodology for lithium recovery for several decades. However, industrial application of solvent extraction for lithium recovery has so far been limited. In contrast, direct lithium extraction using adsorbents based on inorganic minerals has rapidly advanced from research to commercialization. A comparison of solvent extraction processes to adsorption highlights these issues and explains the preference for adsorbents. Although the application of solvent extraction has been criticized for use of large amounts of acid, alkali, and organic solvents, steady progress has been made to improve its potential for industrial lithium production, spurred on generally by the advantages of solvent extraction in selectivity and throughput. Previously developed beta-diketone, organophosphate, and crown ether ligands are being adapted and improved. Their novel use with ionic liquids, deep eutectic solvents, and membrane technologies promises to expand capabilities for extraction of lithium from dilute aqueous sources while improving sustainability. Possibilities for further discovery and innovation abound. In this review, we provide a unique perspective from the field of solvent extraction starting with fundamentals such as ion-transfer theory and apply them to understanding lithium selectivity and extraction behavior. In conclusion, the results are cast in the light of the practical realities of developing economical solvent extraction processes.

Brine↗