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DOE OSTI2025

RANGE: A robust adaptive nature-inspired global explorer of potential energy surfaces

With the growing demand for realistic representations of chemical structures and the advent of exascale computing, the intelligent sampling of potential energy surfaces and efficient identification of global minima have become more essential but also more feasible. Building on prior studies demonstrating the efficiency of the Artificial Bee Colony (ABC) swarm intelligence algorithm, we report a hybrid metaheuristic framework that integrates the adaptive exploration capabilities of ABC coupled with the exploitation strengths of genetic algorithms (GA) in a scalable, Python-based implementation. The resulting tool, RANGE (Robust Adaptive Nature-inspired Global Explorer), provides seamless interfaces to multiple potential energy evaluators, either directly or via widely used Python libraries, and is designed for high-performance computing environments. We describe the implementation details of RANGE and evaluate its performance, relative to ABC- or GA-alone based algorithms, on a variety of chemical systems, including molecular clusters and heterogeneous surfaces. In conclusion, our results demonstrate RANGE’s efficiency, robustness, and broad applicability in addressing challenging global optimization problems in computational chemistry and materials science.

Algorithms and data structure↗

DOE OSTI2026

A Boundary Element Model for Assessing Large‐Scale Pressurization in Faulted Geological Storage Systems

Assessing large-scale pressurization at the regional scale—a possible outcome of large subsurface storage applications such as wastewater injection and geological carbon sequestration—presents significant computational challenges. These challenges are particularly pronounced when accounting for complex geologic structures with multiple reservoir and caprock layers, fault zones, and wells. This study introduces a computationally efficient model that integrates single-phase semi-analytical solutions with a boundary element (BE) approach. The model simulates pressure propagation in multilayered 3D systems, including vertical faults, caprock, basement, and confining units. We apply this new model to a representative scenario involving CO 2 injection near a partially sealing fault with verification against an independent two-phase flow model. Results demonstrate that our model accurately captures far-field pressure responses and that, outside the CO 2 plume zone, pressure predictions from single-phase and two-phase models are nearly identical. This supports the use of single-phase models like ours for efficient estimation of far-field pressure changes. Additionally, we demonstrate its effectiveness at a large scale, incorporating multiple wells and faults. With its ability to represent multiple wells, fault zones, and geological heterogeneity, our model is well suited for assessments of basin-scale pressurization. Its computational efficiency also makes it a promising tool for integration with optimization frameworks aimed at designing and managing injection strategies in faulted storage systems.

Cihan, A. [Lawrence Berkeley National Laboratory (↗

DOE OSTI2025

The Development of Catalysts for Upgrading of Pyrolysis Vapor for Refinery Feedstocks and Intermediates (CRADA Final Report)

Catalytic fast pyrolysis (CFP) is a versatile technology platform to convert biomass into fungible hydrocarbon transportation fuels and chemical co-products. Key technical barriers to reaching this goal include increasing the product yields and achieving the desired fuel properties for gasoline, diesel, and jet range fuels or blendstocks that would be suitable for introduction into existing refinery unit operations. Overcoming these barriers will require durable catalysts that are effective at upgrading and stabilizing biomass pyrolysis vapors. Towards these goals, this CRADA leveraged NREL experience as a leader in biomass pyrolysis research and Johnson Matthey's (JM) experience as a leader in the production of advanced catalytic materials. The scope spanned CFP catalyst development, characterization, multi-scale reaction testing, and computational modeling. CRADA benefits to DOE, Participant, and U.S. Taxpayer: Assists laboratory in achieving programmatic scope, Uses the laboratory’s core competencies. The purpose of this CRADA was to develop and deploy catalysts for biomass CFP to help achieve cost-competitive biofuels and bio-based products. This was accomplished through a close collaboration between biomass conversion researchers at NREL and catalyst development researchers at JM. Summary of Research Results: Focus Area 1. Foundational research on catalytic conversion and deactivation: Key interactions between pyrolysis vapors and heterogeneous catalysts were probed through catalyst characterization, model compound reaction testing, and atomistic-scale computational modeling. Catalyst development focused on multifunctional materials, which include zeolites, oxides, carbides, and nitrides. Computational modeling identified reaction mechanisms and elucidated surface chemistry to test hypotheses regarding mechanisms of deoxygenation, coupling, cracking, dehydration, coke formation, hydrogen transfer, and aromatic ring reactions. This information was used to design multifunctional catalysts to increase product yields, control product selectivity, and reduce deactivation during CFP and downstream processing steps. The results served to increase fundamental understanding of key catalyst attributes and durability features for the upgrading of biomass pyrolysis vapors. Model compound experiments confirmed the importance of metal-acid bifunctionality for the deoxygenation of lignin-derived phenolic species under hydrodeoxygenation conditions. This insight led to the development of catalysts such as Pt/TiO2 and Mo2C, which were confirmed as high-performing materials during subsequent bench-scale experiments using biomass-derived pyrolysis vapors. This focus area also led to the identification of important catalyst deactivation mechanisms associated with the deposition of inorganic contaminants such as potassium. The molecular-level insight from model compound experiments and computational modeling, shown in Figure 1, informed the development of regeneration procedures that have been shown to be effective for restoration of > 90% of initial catalyst activity. This understanding has subsequently been translated to other catalyst systems, including zeolite materials that can be operated without requirements for co-fed hydrogen.

09 BIOMASS FUELS↗

DOE OSTI2024

Correcting implicit solvation at metal/water interfaces through the incorporation of competitive water adsorption

Conventional continuum solvation models are ubiquitous in computational catalysis, including for describing metal/water interfaces, which are relevant to both solution-phase heterogeneous catalysis and electrocatalysis. Nonetheless, we find that such continuum models qualitatively fail to describe both the adsorption free energy and conformational preference for many organic molecules at such interfaces, largely due to the failure of continuum models to incorporate the role of competitive water adsorption. We develop a simple phenomenological model that accounts for competitive water adsorption and show that the model, when used in conjunction with continuum solvation, provides a dramatic improvement in the description of both adsorption and conformational preference. The model is also extended to additionally incorporate the influence of applied potential at the electrode surface, thus facilitating computationally efficient applications to scenarios including electrocatalysis.

Chemistry↗

DOE OSTI2025

Enabling Innovative Analysis on Heterogeneous Clusters through HTCdaskgateway

High energy particle (HEP) physics research is going through fundamental changes as we move to collect larger amounts of data from the Large Hadron Collider (LHC). Analysis facilities and distributed computing, through HTCs, have come together to create the next pythonic generation of analysis by utilizing HTCdaskgateway, a Dask gateway extension, allowing users to spawn workers compatible with both their analysis and heterogeneous clusters in line with authentication requirements. This is enabling physicists to engage with scientific python in ways they had not before because of domain specific C++ tools. An example of HTCdaskgateway’s use is Fermilab’s Elastic Analysis Facility.

Chavez, Elise [U. Wisconsin, Madison (main)]↗

DOE OSTI2024

Employing artificial intelligence to steer exascale workflows with colmena

Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelism of a supercomputer by using Artificial Intelligence (AI) to learn from and adapt a workflow as it executes. Colmena allows scientists to define how their application should respond to events (e.g., task completion) as a series of cooperative agents. In this paper, we describe the design of Colmena, the challenges we overcame while deploying applications on exascale systems, and the science workflows we have enhanced through interweaving AI. The scaling challenges we discuss include developing steering strategies that maximize node utilization, introducing data fabrics that reduce communication overhead of data-intensive tasks, and implementing workflow tasks that cache costly operations between invocations. These innovations coupled with a variety of application patterns accessible through our agent-based steering model have enabled science advances in chemistry, biophysics, and materials science using different types of AI. In conclusion, our vision is that Colmena will spur creative solutions that harness AI across many domains of scientific computing.

Workflows↗

DOE OSTI2024

SIREN: Scaling Ion-Traps by REquiring iNnovative Heterogenous Integration

The SIREN (Scaling Ion Traps by Requiring iNnovative heterogenous integration) project explores the feasibility of heterogeneous integration (HI) as a transformative approach to scaling ion traps, a critical technology for advancing quantum computers and atomic clocks. Traditional ion trap architectures face significant challenges in scalability due to limitations in optical access, fabrication techniques, and material constraints. SIREN addresses these challenges by leveraging HI, which combines different materials and fabrication processes to create more complex and efficient ion trap structures. HI integrated structures can be manufactured without compromising the process to maintain compatibility to ion traps. This project focuses on integrating a separately fabricated waveguide with a fully functional ion trap. The respective alignment between the pieces needs to be accurate to less than 2 µm to ensure that the light from the waveguide can overlap with the trapping region. The fine alignment must also be maintained through an ultra-high vacuum bake, a critical step in preparing an ion trap experiment. The project's outcomes suggest that heterogeneous integration is a promising pathway for overcoming current scalability barriers, paving the way for the next generation of quantum technologies. SIREN's findings contribute significantly to the field, offering a scalable solution that could accelerate the development of practical quantum computers and highly accurate atomic clocks.

42 ENGINEERING↗

DOE OSTI2025

Integrating Ultra-Coarse-Grained Protein Models into Accessible Workflows for Multiscale Molecular Dynamics

To capture protein conformational transitions using molecular dynamics (MD), several simulation resolutions covering different spatial and temporal scales are typically needed. All-atom (AA) simulations provide fine resolution, but are computationally infeasible for large systems over longer durations. Coarse-grained (CG) and ultra-coarse-grained (UCG) models have a lower resolution and computational cost while still being able to conserve essential protein features. Prior work on a Multiscale Machinelearned Modeling Infrastructure (MuMMI) combined both AA and CG simulations to study RAS-RAF protein interactions, leveraging CG models for longer time scales and using AA to investigate unusual conformations in greater detail. However, MuMMI is still resource-intensive, and this study aims to maximize exploration of the protein conformational space while reducing computational cost. In this paper, we build on prior work that integrates UCG models based on heterogeneous elastic network modeling (hENM) into the MuMMI workflow. We demonstrate that UCG models enable accurate sampling of protein conformations, focusing on simulating RAS-RAF protein interactions. Using higher-resolution CG Martini simulation data, we can automatically refine intramolecular interactions in UCG models. We present a scalable Python package that uses fluctuations observed in higher-resolution CG Martini simulations to estimate bond coefficients of the UCG model. We built novel machine learning-based backmapping methods to recover more detailed CG Martini structures from UCG structures, using diffusion models to learn the mapping between scales. Finally, we present UCG-mini-MuMMI, an accessible and less compute-intensive version of MuMMI as a resource for the scientific community. Incorporating UCG models into MD studies is applicable to a broad range of systems and proteins, and our study offers insights into the advantages and limitations of these methods.

Chemical structure↗

DOE OSTI2024

Introducing SpaceNet 9 - Cross-Modal Satellite Imagery Registration for Natural Disaster Responses

Computer vision algorithms are increasingly leveraged to accelerate geospatial analysis for disaster response and recovery. As the diversity of remote sensing imagery grows with optical, SAR, and other modalities, a perquisite for analytics is cross-modal image registration. There is a high potential to harness computer vision for this pre-processing requirement toward enabling downstream analytics such as heterogeneous change detection, automated feature extraction, and data fusion. Advancement in these areas has the potential to simplify data wrangling tasks and further accelerate disaster response timelines. The SpaceNet 9 challenge (launching in mid-2024) focuses on addressing the cross-modal image registration problem and demonstrating the utility of such modules on earthquake impacted scenarios. This paper describes the motivation for the SpaceNet 9 and provides a first overview of the dataset, the baseline algorithm, and implications for seeking cross-modal image registration in Earth observation. Code is available at https://github.com/SpaceNetChallenge/SpaceNet9.

Hansch, Ronny↗

DOE OSTI2025

Nanotomography for Quantitative 3D Particle Reconstruction

Particulates are ubiquitous across fuel cycle operations and carry critical information about particle formation, processing, and potential proliferation-related activities. Traditional analytical techniques, including micro-Raman spectroscopy and standard electron microscopy, are often limited in spatial resolution or dimensionality, particularly when used to examine metallic or submicron-scale features. Understanding particle morphology, phase distribution, and internal porosity is essential for constraining formation conditions, thermodynamic environments, and material transport behavior. In this report, we demonstrate the application of plasma focused ion beam nanotomography to reconstruct micron-scale particulates at nanoscale resolution. Using high-resolution backscattered electron imaging and Avizo software, we obtained 3D reconstructions that enabled quantitative analysis of particle morphology, phase composition, and internal voids. Representative examples include a Ta particle with a large central void and a composite particle with embedded tetrahedral crystalline structures. These reconstructions reveal structural and compositional details that are inaccessible through conventional 2D imaging. The results demonstrate that nanotomography provides both qualitative and quantitative insights into particle formation and behavior. Using nanotomography, porosity and phase distributions can be quantified to inform models of particle density, transport, and solidification conditions. Beyond technical insights, the workflow developed here establishes a transferable capability for analyzing heterogeneous particles and has potential applications in bulk materials studies via x-ray computed tomography or other volumetric imaging modalities. Ongoing efforts are focused on optimizing the workflow to process multiple particles simultaneously, increasing throughput and statistical robustness. Overall, this work illustrates the power of nanotomography as a tool for connecting particulate morphology to formation mechanisms, composition, and transport, thereby strengthening analytical capabilities for nuclear forensics, fuel cycle analysis, and related scientific investigations.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

Towards Scaling Law Analysis For Spatiotemporal Weather Data

Compute-optimal scaling laws are relatively well studied for NLP and CV, where objectives are typically single-step and targets are comparatively homogeneous. Weather forecasting is harder to characterize in the same framework: autoregressive rollouts compound errors over long horizons, outputs couple many physical channels with disparate scales and predictability, and globally pooled test metrics can disagree sharply with per-channel, late-lead behavior implied by short-horizon training. We extend neural scaling analysis for autoregressive weather forecasting from single-step training loss to long rollouts and per-channel metrics. We quantify (1) how prediction error is distributed across channels and how its growth rate evolves with forecast horizon, (2) if power law scaling holds for test error, relative to rollout length when error is pooled globally, and (3) how that fit varies jointly with horizon and channel for parameter, data, and compute-based scaling axes. We find strong cross-channel and cross-horizon heterogeneity: pooled scaling can look favorable while many channels degrade at late leads. We discuss implications for weighted objectives, horizon-aware curricula, and resource allocation across outputs.

Kiefer Jr, Alexander [ORNL] (ORCID:000000025398874↗

DOE OSTI2024

Finding MIDDLE Ground: Scalable and Secure Distributed Learning

Edge computing methods allow devices to efficiently train a high-performing, robust, and personalized model for predictive tasks. However, these methods succumb to privacy and scalability concerns such as adversarial data recovery and expensive model communication. Furthermore, edge computing methods unrealistically assume that all devices train an identical model. In practice, edge devices have varying computational and memory constraints which may not allow certain devices to have the space or speed to train a specific model. To overcome these issues, we propose MIDDLE: a model independent distributed learning algorithm which allows heterogeneous edge devices to assist each other’s training while communicating only non-sensitive information. MIDDLE unlocks the ability for edge devices, regardless of computational or memory constraints, to assist each other even with completely different model architectures. Furthermore, MIDDLE does not require model or gradient communication which greatly reduces communication size and time. We prove that MIDDLE attains the optimal convergence rate O(1/sqrt(TM)) of stochastic gradient descent for convex and non-convex smooth optimization (for total iterations T and batch size M). Finally, our experimental results demonstrate that MIDDLE (even in non-IID data settings) attains robust and high-performing models without model or gradient communication.

Bornstein, Marc I.↗

DOE OSTI2026

InterQnet: A Heterogeneous Full-Stack Approach to Co-Designing Scalable Quantum Networks

Quantum communications have progressed significantly, moving from a theoretical concept to small-scale experiments to recent metropolitan-scale demonstrations. As the technology matures, it is expected to revolutionize quantum computing in much the same way that classical networks revolutionized classical computing. Quantum communications will also enable breakthroughs in quantum sensing, metrology, and other areas. However, scalability has emerged as a major challenge, particularly in terms of the number and heterogeneity of nodes, the distances between nodes, the diversity of applications, and the scale of user demand. This article describes InterQnet, a multidisciplinary project that advances scalable quantum communications through a comprehensive approach that improves devices, error handling, and network architecture. InterQnet has a two-pronged strategy to address scalability challenges: InterQnet-Achieve focuses on practical realizations of heterogeneous quantum networks by building and then integrating first-generation quantum repeaters with error mitigation schemes and centralized automated network control systems. The resulting system will enable quantum communications between two heterogeneous quantum platforms through a third type of platform operating as a repeater node. InterQnet-Scale focuses on a systems study of architectural choices for scalable quantum networks by developing forward-looking models of quantum network devices, advanced error correction schemes, and entanglement protocols. Here, we report our current progress toward achieving our scalability goals.

Chung, Joaquin [Argonne] (ORCID:0000000173833810)↗

DOE OSTI2025

The Artificial Scientist: in-Transit Machine Learning of Plasma Simulations

Large-scale simulations or scientific experiments produce petabytes of data per run. This poses massive challenges for I/O and storage when scientific analysis workflows are run manually offline. Unsupervised deep learning-based techniques to extract patterns and non-linear relations from these large amounts of data provide a way to build scientific understanding from raw data, reducing the need for manual pre-selection of analysis steps, but require exascale compute and memory to process the full dataset available. In this paper, we demonstrate a heterogeneous streaming workflow in which plasma simulation data is streamed directly to a Machine Learning (ML) application training a model on the simulation data in-transit, completely circumventing the capacity-constrained filesystem bottleneck. This workflow employs openPMD to provide a high level interface to describe scientific data and also uses ADIOS2, to transfer volumes of data that exceed the capabilities of the filesystem. We employ experience replay to avoid catastrophic forgetting in learning from this non-steady state process in a continual manner and adapt it to improve model convergence while learning in-transit. As a proof-of-concept, we approach the ill-posed inverse problem of predicting particle dynamics from radiation in a particle-incell (PIConGPU) simulation of the Kelvin-Helmholtz instability (KHI). We detail hardware-software co-design challenges as we scale PIConGPU to full Frontier, the Top-1 system as of June 2024 Top500 list.

Kelling, Jeffrey [Helmholtz-Zentrum Dresden Rossen↗

DOE OSTI2025

Multiscale Nuclear-Electronic Orbital Quantum Dynamics in Complex Environments

Many renewable energy conversion processes rely on the movement of protons as well as electrons through either electrocatalysis or photoexcitation. The simulation of such processes requires a quantum mechanical description of coupled nuclear-electronic dynamics in a solvent or heterogeneous chemical environment. The overall objective of this project is the development of theoretical and computational capabilities for simulating nuclear-electronic quantum dynamics in complex environments and the creation of high-performance, open-source software. This multiscale framework will enable simulations of the real-time dynamics of nonequilibrium excited state proton-coupled electron transfer, quantum decoherence, vibronic energy transfer, and ultrafast radiolysis, as well as their associated time-resolved multidimensional spectroscopies. An important outcome of this project will be a sustainable, reusable, and interoperable open-source software ecosystem. This software will be designed for emerging exascale and future national leadership computers. Another key outcome will be a multiscale quantum dynamics method and software enabling simulations of nonequilibrium nuclear-electronic quantum dynamics in complex environments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Integrating Analytical Solutions and U-Net Model for Predicting Groundwater Contaminant Plumes in Pump-and-Treat Systems

Pump-and-treat (P&T) is a common technique for groundwater remediation involving the extraction and treatment of contaminated water above ground. Optimizing the design and operation of the P&T well network is essential for maximizing the system’s effectiveness and efficiency. However, this optimization often necessitates many model evaluations, leading to computationally demanding tasks. This study introduces a novel approach that integrates analytical solutions for groundwater dynamics with the U-Net (Ronneberger et al., 2015) deep learning framework to predict groundwater contaminant plume migration under dynamic pumping conditions. By incorporating the Thiem equation (Thiem, 1906) into the input preprocessing, the U-Net model transforms sparse well data into a continuous spatial field that captures the hydraulic impacts of pumping activities. This integration enables the model to leverage both deep learning capabilities and classical physics-based groundwater theories, enhancing prediction accuracy and computational efficiency. These advancements can facilitate rapid, large-scale evaluations of P&T optimization simulations, allowing for timely and effective decision-making in well placement and system management. We demonstrate the model's robust performance across both simplified transient 2D models and a more complex 3D heterogeneous site model at the 200 West P&T facility at the Hanford Site. The U-Net-based model offers substantial computational advantages, reducing simulation times significantly compared to full physics-based models and providing a powerful tool for rapid site evaluation and P&T system optimization, such as evaluating alternative P&T well network designs. Our findings highlight the potential of advanced machine learning models to significantly enhance the efficiency and sustainability of groundwater remediation efforts, offering a novel application of U-Net architecture in environmental science.

Pump-and-treat↗

DOE OSTI2025

CryoDRGN-AI: neural ab initio reconstruction of challenging cryo-EM and cryo-ET datasets

Proteins and other biomolecules form dynamic macromolecular machines that are tightly orchestrated to move, bind, and perform chemistry. Cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET) can access the intrinsic heterogeneity of these complexes and are therefore key tools for understanding their function. However, 3D reconstruction of the collected imaging data presents a challenging computational problem, especially without any starting information, a setting termed ab initio reconstruction. Here, in this study, we introduce cryoDRGN-AI, a method leveraging an expressive neural representation and combining an exhaustive search strategy with gradient-based optimization to process challenging heterogeneous datasets. Using cryoDRGN-AI, we reveal new conformational states in large datasets, reconstruct previously unresolved motions from unfiltered datasets, and demonstrate ab initio reconstruction of biomolecular complexes from in situ data. With this expressive and scalable model for structure determination, we hope to unlock the full potential of cryo-EM and cryo-ET as a high-throughput tool for structural biology and discovery.

Levy, Axel [Stanford Univ., CA (United States); SL↗

DOE OSTI2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment

Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new algorithms, and optimizing resource allocation strategies. However, existing simulators suffer from limited scalability, hardwired algorithms, lack of real-time monitoring, and inability to generate datasets suitable for modern machine learning approaches. We present CGSim, a simulation framework for large-scale distributed computing environments that addresses these limitations. Built upon the validated SimGrid simulation framework, CGSim provides high-level abstractions for modeling heterogeneous grid environments while maintaining accuracy and scalability. Key features include a modular plugin mechanism for testing custom workflow scheduling and data movement policies, interactive real-time visualization dashboards, and automatic generation of event-level datasets suitable for AI-assisted performance modeling. We demonstrate CGSim’s capabilities through a comprehensive evaluation using production ATLAS PanDA workloads, showing significant calibration accuracy improvements across WLCG computing sites. Scalability experiments show near-linear scaling for multi-site simulations, with distributed workloads achieving 6 × better performance compared to single-site execution. The framework enables researchers to simulate WLCG-scale infrastructures with hundreds of sites and thousands of concurrent jobs within practical time budget constraints on commodity hardware.

Vatsavai, Sairam Sri [Brookhaven National Laborato↗