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DOE OSTI2024

Tailoring Carbide Dispersed Steels: A Path to Increased Strength and Hydrogen Tolerance

The use of transition metal carbides is reported for use as a hydrogen trapping mechanism for ferritic and austenitic steel materials. The program combined computational modeling and simulations to guide experiments towards candidate metal carbide traps, both for interfacial and interior trapping. It was found that interfacial trapping is less effective than interior trapping, with the group IVB transition metal carbides being the most effect internal traps with a loss of carbon. The sub-stoichiometric rocksalt structure accommodate the hydrogen atoms in its octahedral interstices. Using percolation theory, carbon loss of approximately 25% or more was sufficient to ensure an interconnected network of vacancies for such trapping from the surface to the internal sites within the carbide. Using this as a guide, the program developed a means to provide a uniform dispersion of ZrC nanoparticles with either Fe or 304L micron-scale powders which was then consolidated by direct current sintering. Electrolytic hydrogen diffusivity studies confirmed the reduction of hydrogen diffusion in the matrix with increasing ZrC content, which was a linear response over the sample range studied (0.01 to 1.0 wt.%). The consolidated material was micro-tensile tested in either a non-hydrogen or hydrogen charge condition and compared to a control with no carbides. Additions up to 0.05 wt.% ZrC increased the yield strength with no loss in ductility in either the non-hydrogen or hydrogen tested condition. ZrC concentrations above this amount further increased the yield strength at the expense of ductility. While these samples had a lower absolute ductility value prior to failure, the relative change in ductility between the non-hydrogen and hydrogen charge states was less for the carbides than that of the control. Metal-rich ZrC nanoparticles were fabricated through a conformal coating process yielding ZrC0.66 particles that were then incorporated into a metal matrix. Notch fatigue testing in a hydrogen environment was conducted where the number of cycles to failure was found to be less in the control than that of the carbide addition. However, the spread in experimental data and the number of samples tested limits a conclusive outcome based on defects noticed in the gauge section of all the powder processed samples. The collective outcomes of this report provide further insight into the mechanisms by which carbides act as hydrogen traps; a means to process such carbides through powder metallurgy; and their associated mechanical performance in either a non-hydrogen or hydrogen-charged condition.

08 HYDROGEN↗

DOE OSTI2025

Accelerators for the Future: R&D at the Fermilab FAST Facility

High energy physics in the U.S. has ambitious plans requiring new and improved accelerator technologies, including an upgraded complex at Fermilab for DUNE, next-generation light sources, and the potential of a future collider to be built on U.S. soil. To address these requirements, Fermilab operates the Fermilab Accelerator Science and Technology (FAST) facility, dedicated to accelerator R&D. FAST includes an electron gun and superconducting RF linac (up to 300 MeV), a storage ring, and an upcoming proton source and injector line (up to 2.5 MeV). In addition to the future of Fermilab accelerators, the broad physics program includes general R&D with potential impact across the DOE science program. This colloquium will provide a basic introduction to accelerator technologies and describe the principles of proton and electron accelerators, including the challenges associated with next-generation operations. We will discuss the exciting R&D ongoing at FAST to address the required technological advancements, focusing on Non-Linear Integrable Optics (NIO) for improved beam intensity and Optical Stochastic Cooling (OSC) for improved beam quality, and touching on a wide range of additional ongoing research. I intend to make the content interesting and accessible to those who have never taken any formal courses in accelerator physics.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗

DOE OSTI2024

Advanced Shuttle Strategies for Parallel QCCD Architectures

Trapped ions (TIs) are at the forefront of quantum computing implementation, offering unparalleled coherence, fidelity, and connectivity. However, the scalability of TI systems is hampered by the limited capacity of individual ion traps, necessitating intricate ion shuttling for advanced computational tasks. The quantum charge-coupled device (QCCD) framework has emerged as a promising solution, facilitating ion mobility for universal quantum computation. Current QCCD architectures predominantly feature a linear topology, which is increasingly recognized as inefficient for complex quantum operations. Anticipating the shift toward more efficacious designs, this article introduces an innovative quantum scheduling strategy optimized for parallel QCCD topologies. Our strategy proposes a probabilistic formula for ion movement, alongside ingenious methods for local layer generation and layer compression, yielding a significant reduction in ion shuttle times. Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities, such as traffic blocks and trap capacity limitations. The consequent reduction in shuttle operations leads to lower energy consumption and an enhancement in the quantum computer's fidelity, ultimately accelerating program execution times.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Low-Alpha Operation of the IOTA Storage Ring

43 PARTICLE ACCELERATORS↗

DOE OSTI2026

Multifunctional electrochemical memory stabilized by phase coexistence

Our growing computing needs, especially in applications that heavily rely on artificial intelligence (AI), motivate a search for new components that could substantially augment the performance of general-purpose digital computers. Beyond ON/OFF switching, new components with linear multistate analog resistive tuning, nonlinear volatile switching, spiking, oscillatory, stochastic and other complex functionalities could enable highly efficient neuromorphic computing schemes for AI information processing. Compared to the extreme multifunctionality of biological neurons, realizing all the above characteristics in a single, scalable analog component remains a grand challenge. Here we investigate electrochemical gating combined with localized thermal activation to program and switch a single, vertically integrated and dimensionally scaled electrothermal chemical random access memory (ETCRAM) with a channel and reservoir composed of phase-separated vanadium oxide. Closely related to electrochemical RAM (ECRAM), ETCRAM uses an integrated gate-heater electrode to overcome kinetic barriers that help retain states at ambient temperatures. In addition to synapse-like stable and programmable analog resistance states arising from redox-tunable phase coexistence, a single component exhibits neuron-like nonlinear conductance switching with a tunable threshold and self-driven dynamics owing to the thermally driven metal-insulator phase transition in vanadium dioxide. More broadly, we demonstrate that electrochemically stabilized phase coexistence could unlock analog electronics with novel functionality, stability, reconfigurability, and scalability.

Oh, Sangheon [Sandia National Lab. (SNL-CA), Liver↗

DOE OSTI2025

Reversible Nanocomposite by Programming Amorphous Polymer Conformation Under Nanoconfinement

Nanoconfinements are utilized to program how polymers entangle and disentangle as chain clusters to engineer pseudo bonds with tunable strength, multivalency, and directionality. When amorphous polymers are grafted to nanoparticles that are one magnitude larger in size than individual polymers, programming grafted chain conformations can "synthesize" high-performance nanocomposites with moduli of ≈25GPa and a circular lifecycle without forming and/or breaking chemical bonds. These nanocomposites dissipate external stresses by disentangling and stretching grafted polymers up to ≈98% of their contour length, analogous to that of folded proteins; use both polymers and nanoparticles for load bearing; and exhibit a non-linear dependence on composition throughout the microscopic, nanoscopic, and single-particle levels.

Chen, Tiffany↗

DOE OSTI2025

Upgrading Fermilab s Accelerator Control System with ACORN

The Fermilab Accelerator Complex is the largest national user facility in the Office of High Energy Physics (DOE/HEP) program and the only national user facility operating at Fermilab. Fermilab serves as the host to the Long Baseline Neutrino Facility/Deep Underground Neutrino Experiment (LBNF/DUNE), the laboratory’s flagship project for neutrino science that is under construction. LBNF/DUNE will be powered by megawatt beams from an upgraded accelerator, the Proton Improvement Plan II (PIP-II) that will replace the laboratory’s aging linear accelerator with a new one based on superconducting radio-frequency cavities. The Accelerator Controls Operations Research Network (ACORN) Project will support LBNF/DUNE and PIP-II by modernizing the accelerator control system. The project is at the conceptual design phase and looking to achieve Critical Decision 1 (CD-1) later this year. The scope and structure of the project will be presented, along with an overview of how that has changed in the past year. Current design and technology choices will be shared. Specific challenges facing the project will be addressed, along with current thinking on solutions.

Roehrig, Christian [Fermilab]↗

DOE OSTI2026

SEGUID v2: Extending SEGUID checksums for circular, linear, single- and double-stranded biological sequences

Background Synthetic biology involves combining different DNA fragments, each containing functional biological parts, to address specific problems. Fundamental gene-function research often requires cloning and propagating DNA fragments, such as those from the iGEM Parts Registry or Addgene, typically distributed as circular plasmids. Addgene’s repository alone offers around 150,000 plasmids. To ensure data integrity, cryptographic checksums can be calculated for the sequences. Each sequence has a unique checksum, making checksums useful for validation and quick lookups of associated annotations. For example, the SEGUID checksum uniquely identifies protein sequences with a 27-character string. Objectives The original SEGUID, while effective for protein sequences and single-stranded DNA (ssDNA), is not suitable for circular DNA since there is no natural starting position nor for double-stranded DNA (dsDNA) since two separate sequences are present. Challenges include how to uniquely represent linear dsDNA, circular ssDNA, and circular dsDNA. To meet these needs, we propose SEGUID v2, which extends the original SEGUID to handle additional types of sequences. Conclusions SEGUID v2 produces orientation and rotation invariant checksums for single-stranded, double-stranded, possibly staggered, linear, and circular DNA and RNA sequences. Customizable alphabets allow for other types of sequences. In contrast to the original SEGUID, which uses Base64, SEGUID v2 uses Base64url to encode the SHA-1 hash. This ensures SEGUID v2 checksums can be used as-is in filenames, regardless of platform, and in URLs, with minimal friction. Availability SEGUID v2 is readily available for major programming languages, distributed under the MIT license. JavaScript package seguid is available on npm, Python package seguid on PyPi, R package seguid on CRAN, and a Tcl script on GitHub. These tools, along with documentation, examples, and an online SEGUID Calculator , can be found at https://www.seguid.org .

Pereira, Humberto↗

DOE OSTI2025

Dual-ion ECRAM as a stable and accurate analog synapse

Electrochemical random-access memory (ECRAM) works by tuning the bulk electronic conductance of functional materials via reversible, electrochemical insertion of ions, resulting in stable analog resistive switching, attractive for analog in-memory and neuromorphic computing. However, achieving fast programming for training and long retention for inference has been elusive. Protonic ECRAM demonstrates fast programming but insufficient retention, while oxygen-based ECRAM with excellent retention requires elevated programming temperatures. Cu-based ECRAM offers a compromise, with an activation energy (E A ) of ≈0.76 eV between protons (E A ≈ 0.4 eV) and oxygen (E A > 1 eV), enabling extensive retention and room temperature programming. Combining Cu 2+ ions with protons to form a dual-ion ECRAM, we demonstrate two distinct switching behaviors: fast switching at ≤5 V, (E A ≈ 0.45 eV) via protons, and nonvolatile, room temperature switching at ≥8 V, with E A ≈ 0.76 eV via Cu 2+ ions. In conclusion, the Cu-based state exhibits a wide conductance range, with excellent retention, low noise, and linear current-voltage behavior, achieving digital-equivalent ImageNet inference accuracy.

analog in-memory computing↗

DOE OSTI2026

A Simulator for Neyer Tests of Explosives

Explosives and explosive devices such as detonators are typically tested by applying a range of stimuli such as voltage or mechanical shock, and recording binary “detonated/did not detonate” responses. These are analyzed using maximum likelihood or generalized linear models to provide estimates of quantities such as the all-fire and no-fire points. Given that the true threshold for detonation is unknown a priori , sequential design methods are typically used to optimize the set of test points. One popular method, implemented in commercial software, is Neyer’s algorithm. To support simulation and experimental design, we have developed code in the R programming language to duplicate the functions of the Neyer software. We provide code for the simulator along with a description and examples of usage.

42 ENGINEERING↗

DOE OSTI2024

Implementing a Laser Stabilization System for Trapping Ca+ Ions: an Internship Reflection

At Lawrence Livermore National Laboratory, I contributed to a project developing 3D printed micro ion traps for quantum computing. I designed, implemented, and assessed a laser stabilization system that locked lasers to the frequencies required for calibrating our High Finesse WS8-10 wavelength meter and for laser cooling and trapping of Ca+ ions. I also programmed a Python interface for hardware communication, data collection, and statistical analysis. Additionally, I optimized and aligned laser beam paths, and I implemented a closed digital feedback loop using Proportional, Integral, and Derivative (PID) control parameters. I analyzed both the long-term and short-term behavior of our locked lasers and adjusted PID parameters to enhance performance. Furthermore, I used COMSOL to simulate the capacitance of a linear Paul trap design and predict our trap’s performance. The procedures I developed for the interface, analysis, and simulations will continue to support the ion trapping experiment after my appointment. I strengthened my skills in data analysis, Python coding, and optical alignment for laser systems. My confidence as a researcher grew, particularly in communicating my research. This experience taught me the importance of careful planning and consideration in research and solidified my desire to continue exploring novel quantum technology as an undergraduate

42 ENGINEERING↗

DOE OSTI2026

Improving the Confidence in Retrievals of Vertical Distributions of Cloud Condensation Nuclei Number Concentration from ARM Supported by Aircraft In Situ Observations

Accurate quantification of the vertical distribution of cloud condensation nuclei (CCN) number concentrations is critical for improving our understanding of aerosol–cloud interactions. Ground-based Raman lidars operated by the Atmospheric Radiation Measurement (ARM) program, together with surface CCN measurements, are used to retrieve vertically resolved CCN number concentrations (Retrieved Number concentration of CCN, RNCCN). These retrievals rely on several assumptions, including that aerosol composition is vertically homogeneous. To assess this assumption, we developed and tested a framework to infer the dominant aerosol classes/types at different altitudes. This was done by applying a k-Nearest-Neighbors (kNN) algorithm to lidar ratio and linear depolarization ratio measurements from Raman lidar. We evaluated the framework using aircraft aerosol and CCN measurements from the ARM Holistic Interactions of Shallow Clouds, Aerosols, and Land Ecosystems (HI-SCALE) field campaign. The results show that RNCCN performance degrades as vertical aerosol complexity increases, i.e., RNCCN agrees with the aircraft CCN in vertically homogeneous conditions, but closure decreases in layered aerosol structures. To generalize beyond individual examples, we introduce a metric (heterogeneity index) that quantifies the vertical complexity by assessing the variation of inferred aerosol classes/types. Case-level statistics show a tendency for RNCCN and aircraft differences to increase with this metric. By detecting retrievals that are likely compromised by aerosol vertical heterogeneity, the proposed framework improves the interpretability and effective use of RNCCN used for long-term evaluation of models and aerosol–cloud interactions.

Tian, Jingjing↗

DOE OSTI2024

Low-alpha Operation of the Iota Storage Ring

Operation with ultra-low momentum-compaction factor (alpha) is a desirable capability for many storage rings and synchrotron radiation sources. For example, low-alpha lattices are commonly used to produce picosecond bunches for the generation of coherent THz radiation and are the basis of a number of conceptual designs for EUV generation via steady-state microbunching (SSMB). Achieving ultra-low alpha requires not only a high-level of stability in the linear optics but also flexible control of higher-order compaction terms. Operation with lower momentum-compaction lattices has recently been investigated at the IOTA storage ring at Fermilab. A procedure for lowering the ring compaction using the linear optics along with compensations from the higher-order magnets was developed with the aid of a model, and an experimental technique for measuring the momentum compaction was developed. The lowest momentum compaction achieved during the available run-time was $3.4\times10^{-4}$, around 15 times lower than previously operated. These feasibility studies ensure an improved experimental understanding of the IOTA optics and potentially will enable new research programs at the facility.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Polycarbonate‐Based Solid‐Polymer Electrolytes for Solid‐State Sodium Batteries

Solid-polymer electrolytes comprised of polypropylene carbonate (PPC) and varied sodium bis(fluorosulfonyl)imide (NaFSI) salt concentrations are investigated for implementation as a conductive solid polymer electrolyte into solid-state cathode composites utilizing a sodium-layered oxide active material. The ionic conductivity generally increases with NaFSI salt content, reaching ≈1 mS cm −1 at 80 °C at the highest salt concentration (PPC:NaFSI = 0.5:1). Through an all-in-one slurry casting method, Na 2/3 Ni 1/3 Mn 2/3 O 2 cathode composites are fabricated in which the dispersed PPC electrolyte acts as the primary binder. Enabled by a bilayer polymer electrolyte system, cycling performance with the PPC cathode electrolyte is optimized with respect to salt concentration and anode material. The best cyclability is achieved with a moderate salt concentration electrolyte (PPC:NaFSI = 5:1), showcasing an initial capacity of 83 mA h g −1 with a remarkable 80% capacity retention after 150 cycles at C/5 rate and 60 °C. The superior performance of the lower salt concentration electrolyte is attributed to better electrochemical stability, as confirmed by linear sweep voltammetry and online electrochemical mass spectrometry measurements. In conclusion, these results underscore the potential of carbonate-based polymer electrolytes and the importance of balancing electrolyte conductivity and stability in cell design.

25 ENERGY STORAGE↗

DOE OSTI2025

A tri-level distribution locational marginal price-based demand response framework

Here, in this paper, we propose a tri-level, nested, two-stage price-based demand response (PBDR) framework that considers distribution locational marginal price (DLMP) as DR enabler between load-serving entities (LSE), demand response providers (DRPs), and customers in the day-ahead distribution market. It enables LSE and customer interactions by using multiple DRPs, positioned in-between, and independently optimizes their objectives. The problem is formulated using linear power flow with approximated power losses and its application in DLMP as DR pricing. The tri-level problem is solved using a nested reformulation & decomposition (R&D) method and tested on the real Indian-108 bus distribution system under various dynamic pricings. Further, the temporal–spatial variations in DLMPs are assessed using fairness criteria. Numerical analyses demonstrate that DLMP applications can effectively improve economic efficiency, and transparency in DR programs valuation with a favorable fairness margin. The results show that DLMP as DR pricing signal induces (0-2) % variation in DLMP for DR participation up to 10 %. Further, it gives over 90 % fairness over temporal–spatial variation for all the customers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

SAIGE-GPU: accelerating genome- and phenome-wide association studies using GPUs

Genome-wide association studies (GWAS) at biobank scale are computationally intensive, especially for admixed populations requiring robust statistical models. SAIGE is a widely used method for generalized linear mixed-model GWAS but is limited by its CPU-based implementation, making phenome-wide association studies impractical for many research groups. We developed SAIGE-GPU, a GPU-accelerated version of SAIGE that replaces CPU-intensive matrix operations with GPU-optimized kernels. The core innovation is distributing genetic relationship matrix calculations across GPUs and communication layers. Applied to 2068 phenotypes from 635 969 participants in the Million Veteran Program, including diverse and admixed populations, SAIGE-GPU achieved a 5-fold speedup in mixed model fitting on supercomputing infrastructure and cloud platforms. We further optimized the variant association testing step through multi-core and multi-trait parallelization. Deployed on Google Cloud Platform and Azure, the method provided substantial cost and time savings. Source code and binaries are available for download at https://github.com/saigegit/SAIGE/tree/SAIGE-GPU-1.3.3. A code snapshot is archived at Zenodo for reproducibility (DOI: [10.5281/zenodo.17642591]). SAIGE-GPU is available in a containerized format for use across HPC and cloud environments and is implemented in R/C++ and runs on Linux systems.

Rodriguez, Alex [Argonne National Laboratory (ANL)↗

DOE OSTI2024

Accelerated Steam Methane Reforming by Dynamically Applied Charges

Catalyst design has traditionally focused on tuning active site properties to optimally bind reaction intermediates and balance the kinetic requirements of multiple competing chemical processes, as necessitated by the Sabatier principle. It has recently been proposed that for reactions following certain potential energy landscapes, the activity limit imposed by the Sabatier principle may be overcome by using programmed oscillations of surface electron density at the timescales of surface reactions (i.e., “catalytic resonance”). Here, we use a combination of density functional theory (DFT) simulations and transient kinetic models (TKMs) to simulate the kinetics of steam methane reforming (SMR) on Ru(211) surfaces under statically and dynamically applied charges. DFT-calculated binding energies of SMR intermediates and transition states exhibit strong sensitivity to positively applied charges and follow unique scaling relationships that deviate from linear periodic trends across transition metals. Our simulations demonstrate that applying a small positive charge to Ru dramatically enhances the steady-state turnover frequency (TOF) of SMR by up to 5 orders of magnitude above the TOF observed over neutral Ru. Thus, statically charging Ru catalysts may be an effective strategy to lower the temperature requirements for SMR. Dynamic square-wave oscillations in charge resulted in SMR catalytic resonance with an onset frequency f ∼ 106 Hz and the corresponding average TOFs exceeding the statically charged Ru surface by an additional 15%. Here, based on sensitivity analyses performed for the two end points of oscillation, we propose that dynamic TOF improvement beyond the Sabatier maximum can be expected when the system oscillates between two kinetic regimes that are uniquely controlled by distinct elementary steps.

Catalysts↗