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DOE OSTI2026

CORE-BFS: Communication-Optimized REctangular-partitioned BFS Achieving 160.845 TeraTEPS on Frontier Supercomputer

Distributed Breadth-First Search (BFS) is fundamental to many large-scale graph applications, but its performance on parallel systems is often limited by high communication overhead. This paper presents CORE-BFS, an extremely scalable GPU-based BFS implementation that introduces a unique rectangular 2D partitioning-based design for Frontier supercomputer. To further improve performance, we propose four key optimizations: (1) Rectangular 2D-partition specific data formats that use two compressed row and one compressed column status array bitmaps combined with a Double Compressed Sparse Row (DCSR) format per partition, reducing memory footprint and inter-rank traffic; (2) Adaptive frontier & communication strategy that unifies top-down and bottom-up traversal on the rectangular layout, uses lazy synchronization in top-down levels, and switches variants based on frontier size to minimize communication overhead; (3) Frontier-split degree-aware update that maps frontier vertices to thread-centric, wavefront-centric, and block-centric kernels based on their degree to improve GPU utilization and memory coalescing; (4) Row-reduction pipeline that overlaps bottom-up adjacency list processing with row-wise bitmap reduction to hide inter-rank latency. Together, these techniques increase parallelism while reducing memory and communication overhead. On the Graph500 benchmark, CORE - BFS scales up to 9,248 Frontier nodes with scale-42 graphs and reaches 160.845 TTEPS, delivering a 5.42 × speedup over our previous Frontier implementation.

Yang, Haoshen [Rutgers University]↗

DOE OSTI2022

Zeolitic Imidazolate Framework Membranes: Novel Synthesis Methods and Progress Toward Industrial Use

In the last decade, zeolitic imidazolate frameworks (ZIFs) have been studied extensively for their potential as selective separation membranes. In this review, we highlight unique structural properties of ZIFs that allow them to achieve certain important separations, like that of propylene from propane, and summarize the state of the art in ZIF thin-film deposition on porous substrates and their modification by postsynthesis treatments. We also review the reported membrane performance for representative membrane synthesis approaches and attempt to rank the synthesis methods with respect to potential for scalability. To compare the dependence of membrane performance on membrane synthesis methods and operating conditions, we map out fluxes and separation factors of selected ZIF-8 membranes for propylene/propane separation. Finally, we provide future directions considering the importance of further improvements in scalability, cost effectiveness, and stable performance under industrially relevant conditions.

Chemistry↗

DOE OSTI2025

Maps of growing season gross primary production and net ecosystem exchange for Council Road Mile Marker 71, Seward Peninsula, Alaska, [2017-2023]

This data archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025a). Murphy et al. (2025a) evaluated whether incorporating observed Arctic vegetation heterogeneity into ELM, the land model of the Department of Energy’s Energy Exascale Earth System Model (E3SM), improved simulations of tundra carbon cycling. The associated model archive can be found at Murphy et al. (2025b). The study focused on the spatial patterns and net landscape-level growing season productivity and carbon uptake. As part of this evaluation, observationally derived maps of average growing season (June–August) net ecosystem exchange (NEE) and gross primary production (GPP) were developed for the same domain. These maps, which form the dataset described here, integrate eddy covariance flux tower, remote sensing, and vegetation community data to provide spatially explicit benchmarks for model evaluation. The maps provide spatially explicit estimates of average growing season NEE and GPP across 13 tundra vegetation communities within the study domain. By combining flux tower observations with Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral imagery and drone-based normalized difference vegetation index (NDVI), these maps capture the heterogeneity of carbon fluxes associated with different Arctic vegetation types. While they represent average seasonal conditions rather than interannual variability, the maps provide a unique dataset for evaluating model performance, comparing vegetation community contributions to landscape-scale carbon cycling, and supporting regional analyses of Arctic carbon dynamics. This data archive contains 5 m resolution maps of vegetation communities, vegetation community average growing season GPP, and vegetation community average growing season NEE (three *.tif files), a User’s Guide (*pdf file), and Table 1 of the User’s Guide displaying vegetation community coverage and average growing season NEE and GPP values (*.csv file).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗

DOE OSTI2024

IRIS Reimagined: Advancements in Intelligent Runtime System for Task-Based Programming

Task-based programming models are gaining traction in scientific computing. IRIS is a portable runtime system that exploits multiple heterogeneous programming systems and can discover available resources and manage multiple diverse programming systems (e.g., CUDA, Hexagon, HIP, Level Zero, OpenCL, and OpenMP) simultaneously. It accounts for the constraints of task dependencies and provides customizable scheduling policies to map those tasks to heterogeneous devices. In this paper, we present new capabilities added to IRIS to improve its portability for heterogeneous programming, build-friendliness, and performance efficiency. The new additions include vendor-specific kernel support, a runtime system with a foreign function interface to eliminate writing wrapper or boilerplate code for heterogeneous kernels, an easy-to-use and configurable CMake-based build environment, automatic and efficient data transfers and orchestration, and the Hunter and DAGGER toolchains to evaluate IRIS’s task scheduling algorithms.

Miniskar, Narasinga Rao↗

DOE OSTI2025

Formation trajectories of solution-processed perovskite thin films from mixed solvents

The engineering of mixed-solvent formulations and their evaporation conditions are key to reproducible perovskite coatings for high-performance photovoltaics. Here, we report a lumped-parameter evaporation model to predict the evolution of a perovskite ink liquid film over time (solvent ratio, solute concentration, and film thickness). The drying-rate model is validated via in situ film-thickness measurements, and the predicted transient liquid film state is mapped as a process path. These methods allow for the prediction of process sensitivity to local environmental factors and the understanding and visualization of a broader processing parameter space enabled through the coupling of process and ink engineering. Process maps are applied to create a new framework for scalable perovskite coating development with a goal of improving the reproducibility and transferability of perovskite fabrication. This approach is demonstrated with blade-coated FA 0.83 Cs 0.17 PbI 3 photovoltaic devices, improving the photovoltaic conversion efficiency from 17.5% ± 1.7% to 20.3% ± 0.6%.

14 SOLAR ENERGY↗

DOE OSTI2021

Experimental signatures of electron cyclotron wave energy condensation in magnetic islands

Here, bifurcation of the electron temperature perturbation (ΔT e ) in magnetic islands due to electron cyclotron current drive (ECCD) is reported for the first time in toroidal plasmas. In this DIIID experiment ΔT e spontaneously increases by 50% in a growing 2,1 island which in turn rapidly shrinks, consistent with improved stabilization by the ECCD due to the temperature bifurcation. This non-linearity of ΔT e maps out a hysteresis in the island evolution cycle, in qualitative agreement with the theoretically predicted radio frequency current condensation effect. Therefore, this observed bifurcation can be critical for the avoidance of disruptions caused by tearing modes in tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

The Pantheon+ Analysis: Evaluating Peculiar Velocity Corrections in Cosmological Analyses with Nearby Type Ia Supernovae

Separating the components of redshift due to expansion and peculiar motion in the nearby universe (z < 0.1) is critical for using Type Ia Supernovae (SNe Ia) to measure the Hubble constant (H 0 ) and the equation-of-state parameter of dark energy (w). Here, we study the two dominant "motions" contributing to nearby peculiar velocities: large-scale, coherent-flow (CF) motions and small-scale motions due to gravitationally associated galaxies deemed to be in a galaxy group. We use a set of 584 low-z SNe from the Pantheon+ sample, and evaluate the efficacy of corrections to these motions by measuring the improvement of SN distance residuals. We study multiple methods for modeling the large and small-scale motions and show that, while group assignments and CF corrections individually contribute to small improvements in Hubble residual scatter, the greatest improvement comes from the combination of the two (relative standard deviation of the Hubble residuals, Rel. SD, improves from 0.167 to 0.157 mag). We find the optimal flow corrections derived from various local density maps significantly reduce Hubble residuals while raising H 0 by ~0.4 km s -1 Mpc -1 as compared to using CMB redshifts, disfavoring the hypothesis that unrecognized local structure could resolve the Hubble tension. We estimate that the systematic uncertainties in cosmological parameters after optimally correcting redshifts are 0.06–0.11 km s -1 Mpc -1 in H 0 and 0.02–0.03 in w which are smaller than the statistical uncertainties for these measurements: 1.5 km s -1 Mpc -1 for H 0 and 0.04 for w.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Genome-resolved correlation mapping links microbial community structure to metabolic interactions driving methane production from wastewater

Anaerobic digestion of municipal mixed sludge produces methane that can be converted into renewable natural gas. To improve economics of this microbial mediated process, metabolic interactions catalyzing biomass conversion to energy need to be identified. Here, we present a two-year time series associating microbial metabolism and physicochemistry in a full-scale wastewater treatment plant. By creating a co-occurrence network with thousands of time-resolved microbial populations from over 100 samples spanning four operating configurations, known and novel microbial consortia with potential to drive methane production were identified. Interactions between these populations were further resolved in relation to specific process configurations by mapping metagenome assembled genomes and cognate gene expression data onto the network. Prominent interactions included transcriptionally active Methanolinea methanogens and syntrophic benzoate oxidizing Syntrophorhabdus , as well as a Methanoregulaceae population and putative syntrophic acetate oxidizing bacteria affiliated with Bateroidetes (Tenuifilaceae) expressing the glycine cleavage bypass of the Wood–Ljungdahl pathway.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Enhancing the Reversibility of Lattice Oxygen Redox Through Modulated Transition Metal–Oxygen Covalency for Layered Battery Electrodes

Utilizing reversible lattice oxygen redox (OR) in battery electrodes is an essential strategy to overcome the capacity limitation set by conventional transition metal redox. However, lattice OR reactions are often accompanied with irreversible oxygen oxidation, leading to local structural transformations and voltage/capacity fading. Herein, it is proposed that the reversibility of lattice OR can be remarkably improved through modulating transition metal-oxygen covalency for layered electrode of Na-ion batteries. By developing a novel layered P2-Na 0.6 Mg 0.15 Mn 0.7 Cu 0.15 O 2 electrode, it is demonstrated that the highly electronegative Cu dopants can improve the lattice OR reversibility to 95% compared to 73% for Cu-free counterpart, as directly quantified through high-efficiency mapping of resonant inelastic X-ray scattering. Crucially, the large energetic overlap between Cu 3d and O 2p states dictates the rigidity of oxygen framework, which effectively mitigates the structural distortion of local oxygen environment upon (de)sodiation and leads to the enhanced lattice OR reversibility. The electrode also exhibits a completely solid-solution reaction with an ultralow volume change of only 0.45% and a reversible metal migration upon cycling, which together ensure the improved electrochemical performance. Finally, these results emphasize the critical role of transition metal-oxygen covalency for enhancing the reversibility of lattice OR toward high-capacity electrodes employing OR chemistry.

25 ENERGY STORAGE↗

DOE OSTI2023

SwitchX : Gmin-Gmax Switching for Energy-efficient and Robust Implementation of Binarized Neural Networks on ReRAM Xbars

Memristive crossbars can efficiently implement Binarized Neural Networks (BNNs) wherein the weights are stored in high-resistance states (HRS) and low-resistance states (LRS) of the synapses. We propose SwitchX mapping of BNN weights onto ReRAM crossbars such that the impact of crossbar non-idealities, that lead to degradation in computational accuracy, are minimized. Essentially, SwitchX maps the binary weights in such a manner that a crossbar instance comprises of more HRS than LRS synapses. We find BNNs mapped onto crossbars with SwitchX to exhibit better robustness against adversarial attacks than the standard crossbar mapped BNNs, the baseline. Finally, we combine SwitchX with state-aware training (that further increases the feasibility of HRS states during weight mapping) to boost the robustness of a BNN on hardware. We find that this approach yields stronger defense against adversarial attacks than adversarial training, a state-of the-art software defense. We perform experiments on a VGG16 BNN with benchmark datasets (CIFAR-10, CIFAR-100 and TinyImagenet) and use Fast Gradient Sign Method (ϵ = 0.05 to 0.3) and Projected Gradient Descent (ϵ = $\frac{2}{255}$ to $\frac{32}{255}$, α = $\frac{2}{255}$) adversarial attacks. We show that SwitchX combined with state-aware training can yield upto ~35% improvements in clean accuracy and ~6–16% in adversarial accuracies against conventional BNNs. Furthermore, an important by-product of SwitchX mapping is increased crossbar power savings, owing to an increased proportion of HRS synapses, which is furthered with state-aware training. We obtain upto ~21–22% savings in crossbar power consumption for state-aware trained BNN mapped via SwitchX on 16 × 16 and 32 × 32 crossbars using the CIFAR-10 and CIFAR-100 datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗

DOE OSTI2023

Three‐Dimensional Broadband Interferometric Mapping and Polarization (BIMAP‐3D) Observations of Lightning Discharge Processes

Abstract Following on our earlier single‐station, 2‐dimensional (2D) broadband interferometric mapping and polarization (BIMAP) observations of lightning discharges, we recently deployed two BIMAP stations at Los Alamos National Laboratory to map the lightning sources and their polarization in full 3‐dimensional (3D) space (BIMAP‐3D). The two stations are separated by 11.5‐km and each station consists of four antenna sets (instead of three for the original BIMAP) that form a Y ‐shaped array for improved interferometric performance. In this paper, we report the BIMAP‐3D system design, a generalized and analytical 2D interferometry technique for noncoplanar antenna array, a two‐stage 3D mapping technique based on geometric triangulation and baseline‐based differential time of arrival, and a technique to reconstruct the polarization orientation in 3D space by combining the 2D polarization results from the two‐station observations. Along with description of the techniques, we demonstrate and discuss the initial lightning results, including 3D maps for a hybrid intracloud and cloud‐to‐ground flash and for a normal intracloud flash, development of abnormal K‐change leaders, and polarization signatures for a K‐change leader. We find that with the two‐stage 3D mapping techniques, the sources can be located to meters accuracy for a favorable event that occurs between the two stations. We also find the polarization vectors for the example K leader are mostly orthogonal to the leader channel after the full 3D polarization analysis. The main purpose of this paper is to report the BIMAP‐3D techniques and capabilities. Detailed analysis of more specific discharge processes will be reported in later studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Improving constraints on models addressing the Hubble tension with CMB delensing

The Hubble Tension is a well-known issue in modern cosmology that refers to the apparent disagreement in inferences of the Hubble constant H 0 as found through low-redshift observations and those derived from the ΛCDM model utilizing early universe observations. Several extensions to ΛCDM have been proposed to address the Hubble Tension that involve new ingredients or dynamics in the early universe. Reversing the effects of gravitational lensing on cosmic microwave background (CMB) maps produces sharper acoustic peaks in power spectra and allows for tighter constraints on cosmological parameters. We investigate the efficacy of CMB delensing for improving the constraints on parameters used in extensions of the ΛCDM model that are aimed at resolving the Hubble Tension (such as varying fundamental constants, contributions from early dark energy, and self-interacting dark radiation). We use Fisher forecasting to predict the expected constraints with and without this delensing procedure. We demonstrate that CMB delensing improves constraints on H 0 by ~ 20% for viable models and significantly improves constraints on parameters across the board in the low-noise regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Inversion of Time-Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning-Based Approach for Estimating Dynamic Reservoir Property Changes

Carbon capture and storage is being pursued globally as a geoengineering measure for reducing the emission of anthropogenic CO 2 the atmosphere. Comprehensive monitoring, verification, and accounting programs must be established for demonstrating the safe storage of injected CO 2 . One of the most commonly deployed monitoring techniques is time-lapse seismic reservoir monitoring (also known as 4-D seismic), which involves comparing 3-D seismic survey data taken at the same study site but over different times. Analyses of 4-D seismic data volumes can help improve the quality of storage reservoir characterization, track the movement of injected CO 2 plume, and identify potential CO 2 spillover/leakage from the storage reservoirblue. However, the derivation of high-resolution CO 2 saturation maps from 4-D seismic data is a highly nonlinear and ill-posed inverse problem, often requiring significant computational effort. In this research, we apply a physics-based deep learning method to facilitate the solution of both the forward and inverse problems in seismic inversion while honoring physical constraints. A cycle generative adversarial neural network (CycleGAN) model is trained to learn the bidirectional functional mappings between the reservoir dynamic property changes and seismic attribute changes, such that both forward and inverse solutions can be obtained efficiently from the trained model. We show that our CycleGAN-based approach not only improves the reliability of 4-D seismic inversion but also expedites the quantitative interpretation. Our deep learning-based workflow is generic and can be readily used for reservoir characterization and reservoir model updates involving the use of 4-D seismic data.

58 GEOSCIENCES↗

DOE OSTI2020

Shallow EGS Regional Resource Potential and Map - Snake River Plain

SMU Geothermal Lab developed a methodology to estimate shallow (1 km to 4 km) Enhanced Geothermal Systems (EGS) resource potential using an approach that utilizes recent geology and geophysical research along with new well data to improve the thermal conductivity model, mitigate impacts from groundwater flow in the thermal model, and examine radioactivity data variations. By incorporating the results of the most recent projects with the SMU shallow methodology, we developed a more accurate, updated resource estimate for the Snake River Plain (SRP). The resulting maps and resource estimates can be used by the National Renewable Energy Lab (NREL), Bureau of Land Management (BLM), and the public to determine how best to move forward with future project development.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its “fitness” for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction

Abstract Deep learning has emerged as a revolutionary technology for protein residue‐residue contact prediction since the 2012 CASP10 competition. Considerable advancements in the predictive power of the deep learning‐based contact predictions have been achieved since then. However, little effort has been put into interpreting the black‐box deep learning methods. Algorithms that can interpret the relationship between predicted contact maps and the internal mechanism of the deep learning architectures are needed to explore the essential components of contact inference and improve their explainability. In this study, we present an attention‐based convolutional neural network for protein contact prediction, which consists of two attention mechanism‐based modules: sequence attention and regional attention. Our benchmark results on the CASP13 free‐modeling targets demonstrate that the two attention modules added on top of existing typical deep learning models exhibit a complementary effect that contributes to prediction improvements. More importantly, the inclusion of the attention mechanism provides interpretable patterns that contain useful insights into the key fold‐determining residues in proteins. We expect the attention‐based model can provide a reliable and practically interpretable technique that helps break the current bottlenecks in explaining deep neural networks for contact prediction. The source code of our method is available at https://github.com/jianlin-cheng/InterpretContactMap .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Experimental study of compaction localization in carbonate rock and constitutive modeling of mechanical anisotropy

Abstract Sedimentary rocks are inherently anisotropic and prone to strain localization. While the influence of rock anisotropy on the brittle/dilative regime has been studied extensively, its influence on the ductile/compactive regime is much less explored. This paper discusses the anisotropic behavior of a high‐porosity carbonate rock from central Europe (the Maastricht Tuffeau). A set of triaxial tests with concurrent x‐ray tomography has been performed at different confining pressures. The anisotropic characteristics of this rock have been investigated by testing samples cored at different inclinations of the bedding, thus revealing non‐negligible effects of the coring direction on yielding and compaction behavior. Specifically, samples cored perpendicular to bedding display higher strength and longer stages of post‐yielding deformation before manifesting re‐hardening. Despite such alterations of the inelastic response, Digital Image Correlation has revealed that the strain localization mode is independent of the coring direction, thus being primarily affected by the confinement level. To capture the observed interaction between material anisotropy and compaction behavior at the continuum‐scale, an elastoplastic constitutive law has been proposed. For this purpose, a set of tensorial bases has been introduced to replicate how the oriented rock fabric modulates the yielding and plastic flow characteristics of the material. The analyses show that the impact of the coring direction on yield function and plastic flow rule is fundamentally different, thus requiring the use of distinct projection strategies (a strategy here defined heterotopic mapping). The performance of the model, studied through parametric analyses and by calibrating the experimental results, illustrates the improved capability of the proposed constitutive approach when applied to strongly anisotropic porous rocks.

Shahin, Ghassan↗