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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4

Zinc batteries for grid-scale energy storage: Challenges, opportunities, and future directions

The global electricity sector is undergoing rapid transformation, increasing demand for reliable stationary energy storage and intensifying the need for safe, cost-effective, and scalable technologies for grid-scale applications. While lithium-ion batteries currently dominate the market, concerns over cost, safety, and resource availability motivate the exploration of alternative chemistries. Zinc-based batteries have emerged as a promising option due to the abundance, low cost, and wide geographic distribution of zinc, combined with the inherent safety of aqueous electrolytes. This perspective examines the potential of zinc batteries for stationary energy storage, with particular focus on rechargeable zinc-air systems. We discuss the evolution of zinc battery technologies and compare zinc-air, zinc-bromine, nickel-zinc, and aqueous zinc-ion chemistries, highlighting their advantages for grid applications. Key challenges limiting rechargeable zinc battery performance are analyzed, including dendrite formation and corrosion at the zinc anode, electrolyte degradation from carbonation and evaporation, and sluggish oxygen electrocatalysis at the air cathode. Emerging strategies to address these limitations are reviewed, including advanced electrode architectures, electrolyte engineering, catalyst development, and system-level design improvements. Lastly, we outline future research directions and opportunities for developing durable, efficient, and economically viable zinc-based energy storage systems for grid-scale applications.

Aqueous electrolytes↗

In-situ formation of stable interface towards Li-in anode for halide solid-state electrolyte

Halide-based solid-state electrolytes (SSEs) are promising candidates for next-generation all-solid-state lithium batteries (ASSLBs) due to their high ionic conductivity and chemical stability. However, their poor interfacial compatibility with lithium metal anode and Li-In alloy significantly hinder practical application due to the requirement for a protective interlayer. In this study, a novel approach to overcome this limitation is presented by introducing iron (Fe) doping into Li 3 InCl 6 (LIC), which enables direct and stable contact with lithium-indium (Li-In) metal without a protective interlayer. Thermodynamic and computational analyses identified Fe 3+ as a suitable dopant based on its similar reduction potential to In 3+ and structural compatibility within the halide lattice. The synthesized 10 at. % Fe-doped LIC exhibits high phase purity, retained ionic conductivity, and notably improved interfacial stability. Full-cell tests using Fe-LIC achieve over 300 cycles with 80 % capacity retention. At the same time, symmetric Li-In/ Fe-LIC/ Li-In cells sustain over 500 h of operation, representing the first reported long-term cycling of LIC-based ASSLB without a protective interlayer. In conclusion, this work establishes Fe doping as an effective strategy to stabilize halide SSEs of In system against Li-In alloy, thereby simplifying cell architecture and advancing the development of safer, high-performance halide-based solid-state electrolytes.

Halide-based solid-state electrolytes↗

GRinding Automated Classification Engine

This work is an ML-driven framework for automated surface analysis of microscopy images. We create a training dataset by imaging stainless steel samples to benchmark four developed deep neural network architectures. These models, based on a YOLOv8n-cls backend, integrate image features and process metadata using various fusion methods to distinguish between acceptable and unacceptable surface finishes. This code is associated with publication "Classifying Alloy Surface Preparation Quality with Metadata-Infused Machine Learning for Rapid Alloy Discovery" for project APEX LDRD-ER (25-ERD-039)

Gongora, AldairE [Lawrence Livermore National Labo↗

Second Target Station Project: STS Cross-Directorate Workshop on Hydrogen Fuel

Hydrogen, particularly green hydrogen produced through electrolysis using renewable energy, is poised to play a critical role in decarbonizing the global economy. Its ability to address the intermittency of renewable energy sources and decarbonize hard-to-electrify sectors positions it as a vital component of a sustainable energy future. Interest in hydrogen as a clean energy carrier is in a decade of unprecedented growth, with dozens of countries having released national hydrogen strategies contributing to a global hydrogen economy. In 2021, the Department of Energy (DOE) announced the Hydrogen Shot, the first of the Energy Earthshot Initiatives, which aims to lower the cost of clean hydrogen to $\$$1/kg by 2031. This initiative was followed by a considerable increase in funding for hydrogen technologies through the Bipartisan Infrastructure Law (BIL), with $\$$18 billion recently announced for regional demonstration projects (Hydrogen Hubs) and another $\$$11.5 billion aimed at research and development of electrolyzers, fuel cells, manufacturing, and recycling. The 2023 U.S. National Clean Hydrogen Strategy and Roadmap identifies the cost of clean hydrogen as a critical challenge for achieving economic scale. This includes the cost of hydrogen production by electrolysis, delivery and dispensing, onboard storage, and end-use technologies like fuel cells. Fundamental research and development into catalysts, component architectures, and material durability are critical to lower the costs of these vital technologies aimed at achieving a net-zero carbon emission economy by 2050.

08 HYDROGEN↗

A Modular Accelerator Robotics Framework for AD Robotics

Accelerator tunnels, such as the ones at Fermilab, remain highly radioactive after beam shutoff due to induced radiation from the beam. This residual radiation creates a hazardous environment for manual inspection and repair of beamline components. To minimize worker radiation dose and reduce beam downtime, the AD Robotics Initiative previously built a fleet of low-cost custom mobile robots. However, the custom Python sockets server-client architecture lacked standardization, causing development delays and complicating the integration of new sensors and actuators. Here, we developed a modular system using ROS2 and Docker to standardize the teleoperation and control interfaces. This system was validated by implementing a teleoperation controller with real-time, low-latency, and high-definition video feedback. The aim of this framework is for a new feature or even a robot to be integrated into the system simply by documenting the hardware configuration. Current integration of LiDAR, Odometry, and Depth Cameras provides the foundation for Simultaneous Localization and Mapping (SLAM) tasks. Finally, future work involves integration into the accelerator control system and the attachment of a 6 degree-of-freedom robotic arm for telemanipulation.

Rayyan Khan, M. [Fermilab; Rensselaer Poly.; Unlis↗

DarkNESS: A skipper-CCD NanoSatellite for Dark Matter Searches

The Dark matter Nanosatellite Equipped with Skipper Sensors (DarkNESS) deploys a recently developed skipper-CCD architecture with sub-electron readout noise in low Earth orbit (LEO) to investigate potential signatures of dark matter (DM). The mission addresses two interaction channels: electron recoils from strongly interacting sub-GeV DM and X-rays produced through decaying DM. Orbital observations avoid attenuation that limits ground-based measurements, extending sensitivity reach for both channels. The mission proceeds toward launch following laboratory validation of the instrument. A launch opportunity has been secured through Firefly Aerospace's DREAM 2.0 program, awarded to the University of Illinois Urbana-Champaign (UIUC). This will constitute the first use of skipper-CCDs in space and evaluate their suitability for low-noise X-ray and single-photon detection in future space observatories.

Alpine, Phoenix [Illinois U., Urbana (main)] (ORCI↗

Out-of-Pile Testing and Instrumentation Transient Water Irradiation System

Current initiatives to increase the burnup of conventional nuclear fuels past the approximate 62 GWd/t limit have been spurred on by direct savings to refueling and waste storage. The technical justification for a new license limit requires extensive qualification through experimental testing. Unlike beginning-of-life fuels, high-burnup fuels are more susceptible to fuel fragmentation, relocation, and dispersal (FFRD), therefore more data is needed to characterize fuels under key accident scenarios. The Transient Reactor Test Facility (TREAT) located at the Idaho National Laboratory has developed a testing apparatus architecture to test fuels and claddings at prototypic conditions. The Transient Water Irradiation System (TWIST) is the latest iteration of a testing device capable of conducting loss of coolant accidents (LOCAs) in TREAT. The Out-of-Pile Testing and Instrumentation TWIST (OPTI-TWIST) is an electrically heated device that is analogous to TWIST. OPTI-TWIST allows for detailed instrumentation and thermal-hydraulic characterization. TWIST ultimately aims to conduct the most advanced in-situ diagnostics to evaluate FFRD in a prototypic LOCA. Moreover, it will explore the phenomenological bifurcation of a decay-energy heat up driven LOCA and a stored-energy heat up driven LOCA. The instrumentation suite includes conventional thermocouples and pressure transducers in addition to an electro impedance sensor, an acoustic emission sensor, an optical pressure sensor, and an optical pyrometer. Characterizing these instruments in OPTI-TWIST eliminates complications of irradiation effects while preserving extreme thermal-hydraulic conditions. Finally, benchmarking both devices to a thermal-hydraulic code like the Reactor Excursion and Leak Analysis Program (RELAP)5-3D provides a unique opportunity for iteration. Pre-test predictions and post-test interpretations inform the physical designs, operational procedures, test conditions, and instrumentation types and positions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

The future of subsurface monitoring: AEC’s breakthroughs in CCS technology

Carbon capture and storage (CCS) has emerged as a key solution in the fight against climate change. However, for CCS to succeed, it is crucial to ensure that the sequestered CO2 stays safely trapped underground. The U.S. Department of Energy (DOE) has emphasized the need for advancements in subsurface monitoring, measurement, reporting, and verification. Aside from caprock integrity failure, the other primary failure points usually involve defective cement in the casing annulus of wellbores or plugged and abandoned wells. In addition, many energy producers (e.g., oil and gas, geothermal) and storage and disposal operators (e.g., H2 and water) must deal with the same issue. Poorly placed or degraded cement can create pathways for gas or fluid to escape from casing annuli and in plugged and abandoned or orphan wells, posing environmental risks. Yet, a reliable and cost-effective way to monitor cement and well integrity over multiple decades is still unavailable. Traditional geophysical methods like 4D seismic imaging and surface-based electromagnetic monitoring lack the resolution and accuracy for detecting these types of failures (Vasco et al., 2022; Fawad and Mondol, 2021). Wireline logging is expensive to run continuously and is obtrusive to the operation. While fiber optics can potentially be a solution, its bulkiness can significantly compromise the cement's integrity. To address these challenges, the Advanced Energy Consortium (AEC) at The University of Texas at Austin’s Bureau of Economic Geology (the Bureau) has been pioneering research in subsurface monitoring using its portfolio of distributed autonomous microfabricated sensors for harsh subsurface environments since 2008. A class of these microsensors [System on a Chip (SoC)] can be mixed in cement and permanently placed without compromising the cement column; the sensors would then communicate with each other or a data acquisition (DAQ) master node. Another class of the AEC microsensors can be fully autonomous, with rechargeable micro-batteries capable of exceeding 100°C, flash memory, and, currently, a pressure and temperature sensor. They are designed to circulate in mud, geothermal fluids, U-loops, or pipelines. They can log data into memory and are unobtrusive to operations. Our team has been working on a multi-year DOE-funded project (DE-FE0031856)—supported by $2.95M in federal funding and $0.75M in cost-matching from the AEC—to demonstrate SoC sensor utility for CO2 leakage monitoring in CCS applications. This multi-institutional collaboration developed a novel sensing architecture utilizing radiofrequency (RF) microsensors embedded within the cement sheath. These sensors detect CO2 migration and are interrogated via a Smart Casing Collar (SCC).

58 GEOSCIENCES↗

Modular Accelerator Robotics Framework Implementation For Accelerator Inspection

Accelerator tunnels, such as the ones at Fermilab, remain highly radioactive after beam shutoff due to induced radiation from the beam. This residual radiation creates a hazardous environment for manual inspection and repair of beamline components. To minimize worker radiation dose and reduce beam downtime, the AD Robotics Initiative previously built a fleet of low-cost custom mobile robots. However, the custom Python sockets server-client architecture lacked standardization, causing development delays and complicating the integration of new sensors and actuators. Here, we developed a modular system using ROS2 and Docker to standardize the teleoperation and control interfaces. This system was validated by implementing a teleoperation controller with real-time, low-latency, and high-definition video feedback. The aim of this framework is for a new feature or even a robot to be integrated into the system simply by documenting the hardware configuration. Current integration of LiDAR, Odometry, and Depth Cameras provides the foundation for Simultaneous Localization and Mapping (SLAM) tasks. Finally, future work involves integration into the accelerator control system and the attachment of a 6 degree-of-freedom robotic arm for telemanipulation.

Rayyan Khana, M. [Unlisted, US, IL] (ORCID:0009000↗

Autonomous Flow Electrochemistry for Accelerated Catalyst Discovery

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fundamental Studies of the Vibrational, Electronic, and Photophysical Properties of Tetrapyrrolic Architectures

The ability to capture and utilize light in the near-ultraviolet (NUV), visible and near-infrared (NIR-I and NIR-II) spectral regions (i.e., 320–400, 400–700, 700–1000, 1000–1700 nm) is essential for any solar-energy conversion scheme. Nature employs chlorophylls and bacteriochlorophylls in light-harvesting architectures to absorb light in the blue and red/NIR regions. Accessory pigments (carotenoids, bilins) augment absorption of the (bacterio)chlorophylls in the green region. The harvested energy is funneled to a reaction center protein, where charge separation occurs. Subsequent migration of the electron and the hole stabilizes and stores the energy from light via redox chemistry. The long-term objective of the Bocian/Holten&Kirmaier/Lindsey research program under this DOE grant has been to develop tetrapyrrole-based molecular architectures that absorb sunlight, funnel energy and separate charge with high efficiency. Integral to the program has been iterative cycles of design, synthesis and characterization that provided deep insights into the relationships between chemical composition, electronic structure, and key static and dynamic properties (vibrational, redox, photophysical, energy/charge transfer) of tetrapyrrolic systems. Such architectures included monomers, dyads, larger arrays, and complexes with accessory components. The objective was to develop molecular designs and guiding principles to enhance current and future energy-conversion schemes. Molecular arrays targeted to address one or more fundamental questions concerning light harvesting and energy/charge transfer were constructed from analogues of the naturally occurring hemes, chlorophylls and bacteriochlorophylls. Diverse, tunable synthetic building blocks were prepared that spanned the three respective tetrapyrrole families, which are the porphyrins, chlorins and bacteriochlorins. Thus, the research focused on porphyrins as well as synthetic surrogates for chlorophylls (chlorins, 13 1 -oxophorbines and chlorin-imides) and bacteriochlorophylls (bacteriochlorins, bacterio-13 1 -oxophorbines and bacteriochlorin-imides), generically termed hydroporphyrins. Although the three tetrapyrrole classes (porphyrins, chlorins and bacteriochlorins) absorb light strongly in the violet-blue spectral region, the long-wavelength absorption band typically lies in the green-orange, red, and NIR regions, respectively, with increasing intensity. Understanding the spectra, electronic structure, and energy/charge-transfer properties of such tetrapyrrolic macrocycles is of central importance for the rational design of molecular architectures for solar-energy conversion. Our integrated program of molecular design and synthesis coupled with a variety of spectroscopic, electrochemical, and computational studies have probed from first principles how structural and electronic properties of tetrapyrrolic macrocycles dictate spectral properties as well as the rates of ground-state hole/electron transfer and excited-state energy flow in multicomponent architectures. Individual molecules and multicomponent architectures were designed to test ideas of fundamental importance, often requiring the development of new synthetic methodology. The members of the collaborative team had almost daily discussions by phone and/or e-mail concerning design of molecules, flow of compounds between the labs, planning of physical characterization studies, discussing results and analysis and integrating into design of next generation architectures, and the preparation of manuscripts. Furthermore, students and postdocs in the different labs routinely communicated with one another to facilitate the advancement of the research activities. In short, a highly integrated and collaborative research program was well established among the groups. The research effort involved molecular design and synthesis of synthetic molecular architectures by the Lindsey group integrated with physicochemical and photophysical characterization by the Bocian group and the Holten&Kirmaier group (Figure 2). The Bocian group carried out electrochemical, electron paramagnetic resonance (EPR), resonance Raman (RR), and Fourier-transform infrared (FT-IR) studies, as well as density functional theory (DFT) calculations and the time-dependent extension (TDDFT) to gain insight into excited-state properties. The Holten&Kirmaier group carried out static and time-resolved absorption and fluorescence spectroscopy studies and simulated absorption spectra using molecular orbital (MO) energies from DFT as input to the four-orbital model to complement the TDDFT calculations. The combined measurements provided understanding of the vibrational/electronic properties of the individual molecules and the changes that occur upon incorporation into multicomponent architectures. This information underpinned elucidating the mechanisms and timescales of ground-state hole/electron transfer and excited-state energy and charge transfer.

14 SOLAR ENERGY↗

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

From Modular ADMS to Plug-and-Play Ops: Distribution Grid Operations with Platform-Level Orchestration to Enable Ambitious App Hosting

The core function of the distribution grid is to provide electricity to consumers affordably, reliably, and securely. In pursuing these core objectives, distribution utilities are accountable to customers, regulators, and in some cases, shareholders. Other third parties such as aggregators and microgrids can also have a stake in the smooth operation of the grid. Each of these stakeholders has economic, business, and/or governance objectives that inform their expectations of the distribution grid. This multi-objective, multi-stakeholder environment creates tension that must be reconciled to successfully design and operate the distribution grid. Innovative companies are competing to bring high-tech solutions to electric utilities and their customers that address each of these objectives. Many developers of advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS) have adopted a modular architecture that allows grid operators to select functions and features according to their individual system needs. A modular platform also allows the solution provider to develop and integrate specific new product modules; however, the need to pursue multiple objectives with a fixed set of controllable devices makes integration expensive whether it is done at the product development stage or the deployment stage. This cost creates a significant barrier to adoption and can lengthen the product to market time of new solutions. To fundamentally address the complexity of system integration for distribution grid operations, the U.S. Department of Energy Office of Electricity has funded the GridAPPS-D project at PNNL, which streamlines integration by contributing to standards development, defining system architecture, applying advanced mathematics, and developing open-source software to demonstrate the concept of an open data-integration platform for distribution operations. The open data-integration platform concept enables system operators and solution providers to deploy ambitious, best-of-breed applications (or apps) without continually reengineering for integration. Ambitious apps developed by different solution providers will inevitably attempt to achieve different control objectives with the same set of controllable devices. If the open platform itself can resolve these conflicts in a way that achieves the best available outcomes for all apps, doesn’t restrict the ambitious design of apps, and ensures safe and secure operations, apps will be able to plug-and-play with the platform at the same time as other ambitious apps. In this paper, we describe a framework called App Deconfliction that empowers a platform to assign setpoints to controllable devices based on the values preferred by different apps (and even external stakeholder entities like customers or aggregators). The App Deconfliction framework is compatible with several methods for determining setpoint values. We present two methods based on game theory that provide a subtle built-in incentive structure for developers to adapt their apps to the fact that they will be operating in a moderated multi-app environment and to favor device setpoints that have the most effect on their objectives over those that have the least effect. Our simulation-based demonstrations have shown that game-theory-based deconfliction can lead to a 7% improvement in control space utilization compared to design-based methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗

Enzyme property prediction using artificial intelligence

Artificial intelligence (AI)-driven enzyme property prediction enables rapid discovery and engineering of enzymes for a wide range of biotechnological and therapeutic applications. Here, we first introduce the key components in AI model development, including enzyme datasets, protein representation methods, and model architectures. We then highlight a variety of AI tools developed for the prediction of enzyme properties and functional annotations, including enzyme structure, kinetic parameters, substrate specificity, thermostability, solubility, Enzyme Commission number, and Gene Ontology term. Moreover, we describe representative downstream applications enabled by these AI tools. Finally, we discuss some challenges and opportunities as well as future prospects.

Yuan, Le [University of Illinois at Urbana-Champai↗

Quantum Computing Strategy 2026

Quantum computing (QC) is a rapidly maturing technology with the potential for revolutionary impacts on stockpile stewardship science and national security. Recent developments in fault-tolerant architectures have compressed vendor roadmaps, and predictions of a production-ready quantum computer by the mid-2030s are becoming increasingly credible. This strategy provides a roadmap for integrating QC into the Advanced Simulation and Computing (ASC) program by investing in four strategic focus areas: 1. Develop Capabilities in Mission-Relevant Quantum Applications: ASC will prioritize developing quantum-ready applications in mission areas that have shown significant promise for quantum advantage, including simulations of materials in extreme environments, nuclear dynamics, solving linear and nonlinear partial differential equations, and uncertainty quantification. These applications directly support stockpile stewardship science and modernization objectives. 2. Conduct R&D in Algorithms, Software, and Hardware: Sustained research into quantum algorithms, robust software tools, and quantum hardware is essential. ASC will develop efficient quantum algorithms; invest in quantum compilers, debuggers, and performance tools; and explore specialized quantum hardware tailored to NNSA’s unique requirements. 3. Engage with Vendors and Partners: Early and active collaboration with commercial quantum hardware vendors and academic partners is critical. Through testbeds, co-design agreements, and quantum demonstration facilities, ASC will influence hardware design, gain early access to emerging technologies, and ensure that quantum platforms evolve to meet mission needs. 4. Build Knowledge, Experience, and Workforce: Expanding and upskilling the quantum-trained workforce is essential to long-term success. This includes hiring, internal training, university outreach, and postdoctoral support to ensure ASC maintains the expertise required to operate, program, and integrate quantum systems as they become available. While quantum computing will never replace classical computing, it has the potential to solve certain problems with speed and accuracy that would be unachievable using any conceivable classical high-performance computing (HPC) system. By investing strategically in QC, ASC will help propel the emergent QC industry, maintain U.S. technological leadership, ensure mission readiness, and position itself to rapidly adopt quantum technologies as they mature.

97 MATHEMATICS AND COMPUTING↗

Modular System for Direct Conversion of Methane into Methanol via Photocatalysis

In this project, the Recipient’s objective is to develop a liquid phase photocatalytic process for direct conversion of methane into methanol. The specific objectives are to: Develop a bifunctional catalyst using a semiconductor photocatalyst architecture to facilitate methane activation to directly convert methane into methanol. Develop a scalable reactor design to maximize mass transfer and methanol selectivity using an optimized photocatalyst. Develop a conceptual process design for a modular system for flare gas utilization. Conduct comprehensive techno-economic and commercial market assessments to position the technology for commercialization.

03 NATURAL GAS↗

From minimum-viable-products to full models: a step-wise development of diagnostic forward models in support of design, analysis and modelling on the ST40 tokamak

Like most magnetic confined fusion experiments, the ST40 tokamak started off with a small subset of diagnostics and gradually increased the diagnostic set to include more complex and comprehensive systems. To make the most of each operational phase, forward models of various diagnostics are used and developed to aid design, provide consistency-checks during commissioning, test analysis methods, and build workflows to constrain high-level parameters to inform interpretation, theory and modelling. For new models and new analysis workflows, minimum-viable-products are released early, and their complexity is increased in a step-wise manner, facilitating the support of all programme phases on multiple parallel applications, while enabling learning opportunities and feedback loops. In this contribution we review the philosophy, scope and architecture of the framework under development. We discuss the details of some forward models, with examples on how they are used to aid diagnostic design, to investigate analysis methodologies through synthetic data, and how they are embedded in experimental analysis workflows. We compare previously published experimental results with new, more advanced analysis workflows employing more recent, detailed models and new diagnostic data, providing confirmation of the published material from the 2021–22 experimental campaign.

integrated data analysis↗