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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

High-Speed and Hysteresis-Free Near-Infrared Optical Hydrogen Sensor Based on Ti/Pd Bilayer Thin Films

Palladium (Pd) and titanium (Ti) exhibit opposite dielectric responses upon hydrogenation, with stronger effects observed in the near-infrared (NIR) region. Leveraging this contrast, we investigated Ti/Pd bilayer thin films as a platform for NIR hydrogen sensing—particularly at telecommunication-relevant wavelengths, where such devices have remained largely unexplored. Ti/Pd bilayers coated with Teflon AF (TAF) and fabricated via sequential electron-beam and thermal evaporation were characterized using optical transmission measurements under repeated hydrogenation cycles. The Ti (5 nm)/Pd (x = 2.5 nm)/TAF (30 nm) architecture showed a 2.7-fold enhancement in the hydrogen-induced optical contrast at 1550 nm compared to Pd/TAF reference films, attributed to the hydrogen ion exchange between the Ti and Pd layers. The optimized structure, with a Pd thickness of x = 1.9 nm, exhibited hysteresis-free sensing behavior, a rapid response time (t90 < 0.35 s at 4% H2), and a detection limit below 10 ppm. It also demonstrated excellent selectivity with negligible cross-sensitivity to CO2, CH4, and CO, as well as high durability, showing less than 6% signal degradation over 135 hydrogenation cycles. These findings establish a scalable, room-temperature NIR hydrogen sensing platform with strong potential for deployment in automotive, environmental, and industrial applications.

Chemistry↗
DOE OSTI2024

Equivalent-Circuit Models for Grid-Forming Inverters under Unbalanced Steady-State Operating Conditions: Preprint

Positive- and negative-sequence equivalent-circuit models are put forth to capture the operation of grid-forming (GFM) inverters in unbalanced steady-state operating conditions acknowledging the impact of current limiting. The particular control architecture examined adopts droop control (for primary control), nested inner-current and outer-voltage control (in the stationary reference frame), and it is adaptable to two different types of current limiting (current-reference saturation and virtual-impedance limiting). We anticipate the proposed models to be of interest in modeling, analysis, and simulation of GFM inverters in unbalanced settings that may arise, e.g., in the face of faults. Validation of the equivalent-circuit models is pursued via comparison with full-order electromagnetic-transient (EMT) simulations for representative balanced and unbalanced faults.

equivalent-circuit models↗
DOE OSTI2025

An Architectural Survey of the U12G Tunnel Historic District, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), in conjunction with the National Nuclear Security Administration Nevada Field Office (NNSA/NFO), proposes to demolish six buildings and three storage areas located at the U12g Tunnel portal area in Area 12 of the Nevada National Security Site (NNSS). The buildings are 12-358 (Signal Vault); 12-201800 (Storage Quonset Hut); 12-202555 (Walker Shack); 12-868 (Pipe Assembly); 12-B100933 (Electrical Shop); 12-B100944 (Conference Room); and Storage Area 1; Storage Area 2; and Storage Area 3. The buildings and storage areas were selected for demolition as part of the DOE’s Real Property Efficiency Plan to reduce the footprint of unused and non-operational facilities on the NNSS. They are all vacant and have no proposed uses for current or upcoming NNSS missions. Demolition activities constitute an undertaking subject to review under Section 106 of the National Historic Preservation Act (NHPA) (54 United States Code [USC] § 306101) and its implementing regulations, 36 Code of Federal Regulations (CFR) Part 800. Identification efforts began with resources proposed for demolition in federal Fiscal Year (FY) 23. Four buildings were proposed to be demolished in FY23 (12-358, 12-868, 12-201800, and 12-202555). These buildings and the U12g Tunnel Historic District (SHPO No. D444) were recorded in Identification, Evaluation, and Finding of Adverse Effect for the Proposed Demolition of Five Buildings in Area 12, Nevada National Security Site, Nye County, Nevada (Menocal et al. 2023). Identification efforts indicated three buildings (12-358, 12-201800, and 12-868) supported nuclear testing in the U12g Tunnel. The fourth building post-dated the use of U12g Tunnel for nuclear testing activities. The report recommended that three of the four buildings (12-358, 12-201800, 12-868) and the U12g Tunnel Historic District may be eligible for the National Register of Historic Places (NRHP). The report also found that the undertaking would have an adverse effect on the three buildings and on the historic district. The Nevada State Historic Preservation Office (SHPO) concurred with the report’s findings (Reed 2023). The U12g Tunnel was determined eligible as a historic district under the Secretary of the Interior’s (SOI) Significance Criterion A, at the local level, in the context of the Cold War as an underground testing environment for the development of nuclear weapons and to assess the effects of a nuclear explosion on materials and equipment with a period of significance from 1959 to 1971. It was also determined eligible under Significance Criterion C for embodying the distinctive characters of a horizontal tunnel complex used for nuclear testing and as a significant and distinguishable entity. The three buildings were determined to be contributing elements of the district. The undertaking was expanded with the addition of two buildings and three storage areas proposed to be demolished and located within U12g Tunnel Historic District in FY24. These five resources (12-B100933, 12-B100944, and Storage Areas 1, 2, and 3) were recorded in Supplemental Identification, Evaluation, and Finding of Effect for Additional Proposed Demolition at U12g Tunnel, Area 12, Nevada national Security Site, Nye County, Nevada (Brannan et al. 2024). Identification efforts indicated that the two buildings and Storage Area 1 supported nuclear testing in the U12g Tunnel. Storage Area 1 and Storage Area 2 post-dated the nuclear testing activities at U12g Tunnel and were not recommended as contributing elements to the district. The report also found that the undertaking would have an adverse effect on the newly identified buildings and one storage area and on the historic district. The SHPO concurred that the expanded undertaking would result in adverse effects to historic properties (Reed 2025). To resolve these adverse effects, NNSA/NFO, in consultation with the SHPO, is following standard mitigation as stipulated in the 2024 Programmatic Agreement DE-GM58-22NA25554 Among the U.S. Department of Energy and the Nevada State Historic Preservation Officer and the Advisory Council on Historic Preservation Concerning the Protection of Historic Properties on the Nevada National Security Site, Nye County, Nevada (hereafter referred to as the NNSS PA). The standard mitigation measures are outlined in Appendix D of the NNSS PA. As such, this architectural survey has been prepared in accordance with Appendix D of the NNSS PA and follows the report format outlined in Appendix F. It includes a historic context that describes the district’s origin, history, and support functions, its significance in the context of nuclear testing on the NNSS, and identifies contributing and non-contributing elements within the district. The report is accompanied by Architectural Resource Assessment (ARA) forms for individual resources and a Historic District Resource Assessment (HDRA) for the U12g Tunnel Historic District. In total, this architectural report identified 32 primary resources within the district boundary. Six of the primary resources were previously identified as contributing elements. An additional 17 resources are recommended as contributing elements to the district for a total of 23 contributing elements. The other nine resources identified are recommended as non-contributing elements to the district.

12-201800↗
DOE OSTI2026

Coherence-mediated quantum thermometry in a hybrid circuit quantum electrodynamics architecture

Quantum thermometry plays a critical role in the development of low-temperature sensors and quantum information platforms. Here, in this work, we propose and analyze a hybrid circuit quantum electrodynamics architecture in which a superconducting qubit is dispersively coupled to two distinct bosonic modes: one initialized in a weak coherent state as a phase reference and information buffer and the other coupled to a thermal environment. We show that the qubit serves as a sensitive readout of the probe mode, mapping the interplay between thermal and coherent photon-number fluctuations onto measurable dephasing. This coherence-mediated mechanism enables improved sensitivity to thermal energy fluctuations in the sub-millikelvin regime through Ramsey interferometry. We derive analytic expressions for the probe coherence envelope, compute the quantum Fisher information for temperature estimation, and demonstrate numerically that the presence of a coherent reference enhances the qubit's sensitivity to small changes in thermal photon occupancy. Our results establish a coherence-enabled approach to thermometry and provide a scalable platform for future calorimetric sensing in high-energy physics and quantum metrology.

Zhu, Shaojiang [Fermi National Accelerator Laborat↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Human RNome Project draft human RNome sequence of GM12878, B-cell line, obtained by mass-spectrometry sequencing, long-read sequencing and short-read sequencing.

Here we report the first draft of the human RNome sequence, a reference map of RNA chemical modifications in a human B-cell line. RNA carries a diverse repertoire of chemical modifications that regulate gene expression, cellular function, and responses to physiological and pathological cues. Yet, unlike the genome, no reference map of RNA modifications is available for any human cell. To generate this resource, the Human RNome Project Consortium analyzed a shared RNA preparation from the well-characterized GM12878 B-cell line using short-read sequencing, long-read direct RNA sequencing, and mass spectrometry, generating more than 7.1 billion sequencing reads spanning approximately 1.2 trillion nucleotides. The resulting maps of the human RNome reveal that RNA modifications are organized according to function, transcript architecture, and cellular identity. Modifications concentrate at functional centers of ribosomal and transfer RNAs, follow the canonical topology of N6-methyladenosine in coding transcripts, and form coordinated hotspots in immune regulatory genes. This first reference human RNome provides a foundation for understanding how RNA chemistry shapes cellular identity, human disease, and the development of RNA-based therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Lustre Unveiled: Evolution, Design, Advancements, and Current Trends

The Lustre filesystem serves as a vital element in high-performance parallel storage, meeting the rising demands of scientific, research, and enterprise environments. Widely deployed across HPC environments, ranging from small-scale applications in AI/ML, to domains like oil and gas, drug discovery, and meteorology, and manufacturing, Lustre addresses the universal challenge of efficiently accessing vast and ever-increasing volumes of data. Lustre is the filesystem of choice on six out of the top 10 fastest supercomputers in the world today, over 65% of the top 100, and also for over 60% of the top 500. Despite its widespread popularity, there is a lack of a complete and up-to-date reference, covering Lustre’s evolution, design, and various advancements made over the years. In this journal, we aim to fill this gap by providing a comprehensive journey of Lustre, including its history with significant contributions to HPC, detailed architecture and design elements, exploration of advancements added through its evolution, and future directions. Additionally, we present a comparison of Lustre with other prominent storage technologies of the era. To illustrate the current state of Lustre, we analyze several filesystem trends, including utilization, performance, and usage patterns on Orion, the Lustre filesystem on the first exascale supercomputer Frontier. We hope that this journal serves as a comprehensive educational reference for the current and future generations interested in HPC filesystem storage aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the discovery of anomalous behaviors or degradation trends, the identification of the possible causes behind such behaviors and trends, and the prediction of their direct consequences. This paper directly targets the generation of knowledge from ER data by putting “data into context”. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by identifying first which elements of the developed MBSE elements they are referring to. This task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process “knowledge extraction” where our methods to extract knowledge from textual data. Lastly, once numeric and textual ER data elements have been processed and “understood”, we discover possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if there is a temporal relation among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Quantum-Enhanced Dark-Matter Sensing with Large Photon Number Fock States in a High-Q SRF Cavity

Wave-like dark matter candidates such as axions or dark photons in the microwave frequency range can be probed using resonant superconducting cavities. We demonstrate a quantum-enhanced sensing approach based on a multimode, high-Q superconducting RF (SRF) cavity, where a large Fock state is prepared in the storage mode via sideband transitions mediated by a transmon ancilla and a measurement-based feedforward protocol, achieving high-fidelity Fock states [1]. The prepared |n⟩ state induces stimulated emission from the dark-matter field, providing an (n + 1) enhancement in transition probability and proportional boost in signal rate [2]. A secondary cavity mode functions as an in-situ reference to calibrate noise. We present the control sequence, noise-referencing strategy, and projected sensitivity improvements for axion and dark-photon detection. This multimode architecture combines large Fock state preparation with noise calibration, offering a practical path to increased scan rate in cavity-based dark-matter searches.

Kim, Taeyoon↗
DOE OSTI2025

Projected Urban Morphology of the Los Angeles Area by the Year 2100

This dataset provides projections of urban building morphologies for the Los Angeles urban area at 30-meter spatial resolution. It contains 192 raster files that detail two primary building attributes: building footprint fractions (ranging from 0 to 1) and average building heights (ranging from 0 to 75 meters). The projections account for a wide range of future pathways, covering two Shared Socioeconomic Pathway (SSP) scenarios (SSP3 and SSP5), two population scenarios, two developed land intensification scenarios, and four distinct levels of intensification. The dataset was created using dual Generative Adversarial Networks (GANs) trained on 2015 land cover and building properties from the National Land Cover Database (NLCD) and Model America datasets. Supporting information on the dataset has been described in the LAUrbanAreaMorphologyProjections2100_README.txt file.

Pandey, Bhartendu↗
DOE OSTI2026

Automated ICRF heating surrogate modeling via machine learning

This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.

Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗
DOE OSTI2024

Exploring code portability solutions for HEP with a particle tracking test code

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools—including compiler pragma-based approaches, abstraction libraries, and other tools—allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions. While in several cases portable implementations perform close to the reference code version, we find that the performance varies significantly depending on the details of the implementation. Achieving optimal performance is not easy, even for relatively simple applications such as the test codes considered in this work. Several factors can affect the performance, such as the choice of the memory layout, the memory pinning strategy, and the compiler used. The compilers and tools are being actively developed, so future developments may be critical for their deployment in HEP experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Novel Toxin Biosynthetic Gene Cluster in Harmful Algal Bloom-Causing Heteroscytonema crispum : Insights into the Origins of Paralytic Shellfish Toxins

Caused by both eukaryotic dinoflagellates and prokaryotic cyanobacteria, harmful algal blooms are events of severe ecological, economic, and public health consequence, and their incidence has become more common of late. Despite coordinated research efforts to identify and characterize the genomes of harmful algal bloom-causing organisms, the genomic basis and evolutionary origins of paralytic shellfish toxins produced by harmful algal blooms remain at best incomplete. The paralytic shellfish toxin saxitoxin has an especially complex genomic architecture and enigmatic phylogenetic distribution, spanning dinoflagellates and multiple cyanobacterial genera. Using filtration and extraction techniques to target the desired cyanobacteria from nonaxenic culture, coupled with a combination of short- and long-read sequencing, we generated a reference-quality hybrid genome assembly for Heteroscytonema crispum UTEX LB 1556, a freshwater, paralytic shellfish toxin-producing cyanobacterium thought to have the largest known genome in its phylum. We report a complete, novel biosynthetic gene cluster for the paralytic shellfish toxin saxitoxin. Leveraging this biosynthetic gene cluster, we find support for the hypothesis that paralytic shellfish toxin production has appeared in divergent Cyanobacteria lineages through widespread and repeated horizontal gene transfer. This work demonstrates the utility of long-read sequencing and metagenomic assembly toward advancing our understanding of paralytic shellfish toxin biosynthetic gene cluster diversity and suggests a mechanism for the origin of paralytic shellfish toxin biosynthetic genes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

ELECTRONIC STRUCTURE METHODS AND PROTOCOLS WITH APPLICATION TO DYNAMICS, KINETICS AND THERMOCHEMISTRY

Hydrocarbon combustion involves the reaction dynamics of a tremendous number of species beginning with many-component fuel mixtures and proceeding via a complex system of intermediates to form primary and secondary products. Combustion conditions corresponding to new advanced engines and/or alternative fuels rely increasingly on autoignition and low-temperature-combustion chemistry. In these regimes various transient radical species such as HO2, ROO·, ·QOOH, HCO, NO2, HOCO, and Criegee intermediates play important roles in determining the detailed as well as more general dynamics. A clear understanding and accurate representation of these processes is needed for effective modeling. Given the difficulties associated with making reliable experimental measurements of these systems, computation can play an important role in developing these energy technologies. Accurate calculations have their own challenges since even within the simplest dynamical approximations such as transition state theory, the rates depend exponentially on critical barrier heights and these may be sensitive to the level of quantum chemistry. Moreover, it is well-known that in many cases it is necessary to go beyond statistical theories and consider the dynamics. Quantum tunneling, resonances, radiative transitions, and non-adiabatic effects governed by spin-orbit or derivative coupling can be determining factors in those dynamics. Building upon progress made during a period of prior support through the DOE Early Career Program, this project combines developments in the areas of potential energy surface (PES) fitting and multistate multireference quantum chemistry to allow spectroscopically and dynamically/kinetically accurate investigations of key molecular systems (such as those mentioned above), many of which are radicals with strong multireference character and have the possibility of multiple electronic states contributing to the observed dynamics. An ongoing area of investigation is to develop general strategies for robustly convergent electronic structure theory for global multichannel reactive surfaces including diabatization of energy and other relevant surfaces such as dipole transition. Combining advances in ab initio methods with automated interpolative PES fitting allows the construction of high-quality PESs (incorporating thousands of high-level data) to be done rapidly through parallel processing on high-performance computing (HPC) clusters. In addition, new methods and approaches to electronic structure theory will be developed and tested through applications. This project will explore limitations in traditional multireference calculations (e.g., MRCI) such as those imposed by internal contraction, lack of high-order correlation treatment and poor scaling. Methods such as DMRG-based extended active-space CASSCF and various Quantum Monte Carlo (QMC) methods will be applied (including VMC/DMC and FCIQMC). Insight into the relative significance of different orbital spaces and the robustness of application of these approaches on leadership class computing architectures will be gained. Synergy with other components of this research program such as automated PES fitting and multireference quantum chemistry will be used to address challenges encountered by the standard approaches to computational thermochemistry (those being single-reference quantum chemistry and perturbative treatments of the anharmonic vibrational energy, which break down for some cases of electronic structure or floppy strongly coupled vibrational modes).

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

An Evaluation of Representation Learning Methods in Particle Physics Foundation Models

We present a systematic evaluation of representation learning objectives for particle physics within a unified framework. Our study employs a shared transformer-based particle-cloud encoder with standardized preprocessing, matched sampling, and a consistent evaluation protocol on a jet classification dataset. We compare contrastive (supervised and self-supervised), masked particle modeling, and generative reconstruction objectives under a common training regimen. In addition, we introduce targeted supervised architectural modifications that achieve state-of-the-art performance on benchmark evaluations. This controlled comparison isolates the contributions of the learning objective, highlights their respective strengths and limitations, and provides reproducible baselines. We position this work as a reference point for the future development of foundation models in particle physics, enabling more transparent and robust progress across the community.

Chen, Michael [Caltech]↗
DOE OSTI2024

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↗
DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗
DOE OSTI2026

Deep learning for time series forecasting: a survey of recent advances

Time series forecasting plays a critical role in numerous real-world applications, such as finance, healthcare, transportation, and scientific computing. In recent years, deep learning has become a powerful tool for modeling complex temporal patterns and improving forecasting accuracy. This survey provides an overview of recent deep learning approaches for time series forecasting, involving various architectures including RNNs, CNNs, GNNs, transformers, large language models, MLP-based models, and diffusion models. We first identify key challenges in the field, such as temporal dependency, efficiency, and cross-variable dependency, which drive the development of forecasting techniques. Then, the general advantages and limitations of each architecture are discussed to contextualize their adaptation in time series forecasting. Furthermore, we highlight promising design trends like multi-scale modeling, decomposition, and frequency-domain techniques, which are shaping the future of the field. This paper serves as a compact reference for researchers and practitioners seeking to understand the current landscape and future trajectory of deep learning in time series forecasting.

97 MATHEMATICS AND COMPUTING↗