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DOE OSTI2025

Evaluating Chemical Kinetics Predictions for Propane Using 3-D and 0-D Models in a Boosted Spark-Ignited Engine

Propane has been shown to be a promising alternative fuel to reduce emissions while simultaneously achieving high efficiencies in medium- and heavy-duty engines. These high-power density applications require boosted engines which, combined with high compression ratio, can lead to auto-ignition and knock. While three-dimensional (3-D) computational fluid dynamics (CFD) models are often used for resolving the complex fluid flow in engines, these models can become computationally expensive when simulating detailed chemical kinetics. Likewise, zero-dimensional (0-D) models are computationally concise enough for kinetics development, but lack any flow-field information which governs the flame propagation processes in spark ignition (SI) engines. This work presents a comprehensive comparison between 3-D and 0-D closed cycle simulations at knocking conditions in a high compression ratio high stroke-to-bore ratio propane engine. In order to initialize the flow-field for the 3-D closed cycle (intake valve closing, (IVC) to exhaust valve opening, (EVO)) simulation, a motored multi-cycle 3-D model was run using Converge to create a map at IVC, reducing the computational time. The map allowed a non-homogeneous 3-D closed cycle simulation to be satisfactorily validated against experiments, while a homogeneous case using only the turbulence field mapping was also simulated, mimicking 0-D modeling. The 3-D simulations were used to prescribe the initial conditions (e.g., IVC thermodynamics, speciation, burn-rate profile) for a 2-zone 0-D SI engine model in Chemkin Pro for both cases. It was found that 2-zone 0-D modeling underpredicted the knock onset timing, likely due to the lack of thermal stratification in the unburned gas region. Future work will carry multi-zone 0-D modeling to capture the fuel auto-ignition in the unburned region.

Douvry-Rabjeau, Julien [Oakland University, Roches↗

DOE OSTI2025

A fault-tolerant neutral-atom architecture for universal quantum computation

Quantum error correction (QEC) is essential for the realization of large-scale quantum computers. However, owing to the complexity of operating on the encoded ‘logical’ qubits, understanding the physical principles for building fault-tolerant quantum devices and combining them into efficient architectures is an outstanding scientific challenge. Here we use reconfigurable arrays of up to 448 neutral atoms to implement the key elements of a universal, fault-tolerant quantum processing architecture and experimentally explore their underlying working mechanisms. We first use surface codes to study how repeated QEC suppresses errors, demonstrating 2.14(13)x below-threshold performance in a four-round characterization circuit by leveraging atom loss detection and machine learning decoding. We then investigate logical entanglement using transversal gates and lattice surgery and extend it to universal logic through transversal teleportation with three-dimensional [[15,1,3]] codes, enabling arbitrary-angle synthesis with polylogarithmic overhead. Finally, we develop mid-circuit qubit reuse16, increasing experimental cycle rates by two orders of magnitude and enabling deep-circuit protocols with dozens of logical qubits and hundreds of logical teleportations with [[7,1,3]] and high-rate [[16,6,4]] codes while maintaining constant internal entropy. Our experiments show key principles for efficient architecture design, involving the interplay between quantum logic and entropy removal, judiciously using physical entanglement in logic gates and magic state generation, and leveraging teleportations for universality and physical qubit reset. These results establish foundations for scalable, universal error-corrected processing and its practical implementation in neutral atom systems.

atomic and molecular physics↗

DOE OSTI2026

Multipoint Aerostructural Optimization of Wind Turbine Rotors Using a Coupled Blade‐Resolved Aerostructural Solver

Physics‐based design optimization workflows thread the needle between computational cost limitations and simulation complexity, often compromising between modeling detail and the range of operating design conditions. Multipoint aerostructural optimization of wind turbine rotors has so far been confined to low‐fidelity analyses or to high‐fidelity studies with simplified structural models, leaving the most complex design trade‐offs unexplored. We close this gap by performing the first tightly coupled gradient‐based multipoint aerostructural rotor optimization using 3D aerodynamic and structural solvers with discrete coupled adjoints. The optimizer simultaneously varies blade planform, airfoil shapes, and structural thickness through more than 270 design variables, minimizing a weighted combination of rotor mass and power across multiple wind speeds. Applied to a modified DTU 10‐MW benchmark under conservative structural and aerodynamic constraints, our multipoint optimization reduces rotor mass by up to 36% and increases power by 12%–15% across the main operating conditions; biasing the objective toward power yields power gains up to 18% and a 17% mass reduction. For a nominal wind distribution, 3‐point rotor designs accounting for low RPM and high thrust conditions capture dominant trade‐offs and outperform single‐point designs. Adding two off‐design points changes individual‐condition power by less than 3% but leaves the weighted average within 0.5%, and the mass‐power bias has a stronger effect on the final design than the operating‐point weighting itself. Our framework extends naturally to richer load cases and site‐specific wind distributions, providing a basis for high‐fidelity multipoint design earlier in industrial workflows.

17 WIND ENERGY↗

DOE OSTI2025

Off-shell vertices in heavy particle effective theories and B → Dπℓν

We study the modifications to decay amplitudes in heavy to heavy semileptonic decays with multiple hadrons in the final state due to intermediate heavy hadrons being off-shell or having a finite width. Combining Heavy Hadron Chiral Perturbation Theory (HHχPT) with a BCFW on-shell factorization formula, we show that these effects induce O (1/M) corrections to the standard results computed in the narrow-width approximation and therefore are important in extracting form factors from data. A combination of perturbative unitarity, analyticity, and reparameterization invariance fully determine these corrections in terms of known Isgur-Wise functions without the need to introduce new form factors. In doing so, we develop a novel technique to compute the boundary term at complex infinity in the BCFW formula for theories with derivatively coupled scalars. While we have used the $\overline{B}$ → Dπℓν decay as an example, these techniques can generally be applied to effective field theories with (multiple) distinct reference vectors. Article PDF

Chiral Lagrangian↗

DOE OSTI2025

Ten questions on building stock modeling to inform energy efficiency and sustainability

To enhance economic competitiveness and ensure energy efficiency, resilience, and security, cities and governments are adopting technologies and strategies to improve their existing building stocks. This approach aims to reduce energy use, improve energy affordability, and ensure a reliable power supply while safeguarding occupants during extreme weather events that may disrupt energy services. The effectiveness of these solutions will depend on building stock characteristics, use patterns, weather conditions, evolving technologies and their markets, and a city’s socio-economic conditions. This paper presents ten questions and answers that highlight the most important issues regarding the use of building stock modeling as a powerful tool to provide insights for informing stakeholders’ actions and decision-making on energy efficiency, costs reduction, and resilience of buildings in cities. Building stock modeling should build upon the fit-for-purpose framework, balancing the use case accuracy requirements, level of complexity, and needed resources (expertise, compute). The advancements in Artificial Intelligence (AI), the increasingly available open dataset of building stock in cities, and the more affordable powerful computing will accelerate the adoption of building stock modeling across scales by researchers and practitioners to inform decision making on sustainability and efficiency.

AI↗

DOE OSTI2025

Design, Synthesis, and Biological Activity of Novel Ornithine Decarboxylase (ODC) Inhibitors

We here describe the design, synthesis, and biological activity of novel ornithine decarboxylase (ODC) inhibitors that show significantly higher potency in vitro than α-difluoromethylornithine (DFMO), a U.S. Food and Drug Administration (FDA) approved drug. We report two X-ray structures of ODC complexed with new ODC inhibitors, computational docking, molecular dynamics, and binding free energy calculations to validate the experimental models. The X-ray structures reveal that covalent adducts with pyridoxal phosphate (PLP) are formed in the active site of the human ODC enzyme, as verified by their preparation and enzymatic testing. Finally, we verified that the cellular activity of endogenous ODC was inhibited, and polyamine levels were reduced. Given that ODC is a clinically validated target, combined with the fact that DFMO is currently the only ODC inhibitor in clinical use for several indications, the further development of more potent ODC inhibitors with superior activity and physical properties is warranted.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr 0.78 Y 0.22 O 3-δ

Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr 0.78 Y 0.22 O 3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Here, our results reproduce the experimentally observed non-monotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration—two mechanisms that influence the lattice expansion in opposite directions. The importance of this work lies in its detailed demonstration of how advanced computational techniques can accurately capture complex environmental effects, providing a valuable framework for modeling similar phenomena in a variety of material systems and applications.

Proton conducting fuel cell↗

DOE OSTI2026

Circularity in Sequence-Controlled Copolyamides Enabled by Regioselective Enzymatic Hydrolysis

Sequence-controlled polymers enable precise control over macromolecular structures and function, but both their synthesis and end-of-life management remain fundamental challenges. Achieving high sequence fidelity is synthetically demanding, and conventional depolymerization methods lack regioselectivity, leading to irreversible loss of encoded molecular information and limiting polymer circularity. Enzymatic catalysis offers a potential solution by combining substrate specificity with selective bond cleavage. Here, we report the synthesis, characterization, and regioselective enzymatic depolymerization of poly- (X,AMA), a sequence-controlled copolyamide composed of alternating hexamethylenediamine−adipic acid (MA) and pxylylenediamine− adipic acid (XA) repeat units. Poly(X,AMA) was synthesized via solid-state polycondensation (SSP) of sequence-defined oligomers, enabling precise control over repeat-unit order. Polymer microstructure and sequence fidelity were confirmed by 13 C NMR spectroscopy and MALDI−TOF mass spectrometry. Comparison with a statistical copolymer analogue and Nylon-66 demonstrated pronounced differences in crystallinity, morphology, and thermal behavior arising from sequence control. Screening of 96 Nylon hydrolase homologues against poly(X,AMA) revealed strongly enzyme-dependent depolymerization profiles. While tetrad formation was generally favored, enzymes displayed pronounced sequence selectivity, preferentially releasing distinct sequence-defined tetrads XAMA or MAXA. SSP of sequence-defined tetrad MAXA produced a copolyamide with near identical monomer ordering as poly(X,AMA). Computational modeling of enzyme−substrate complexes identified structural features consistent with the observed regioselectivity. Together, these results establish selective enzymatic depolymerization as a viable strategy for the circular recycling of sequence-controlled polymers and provide a foundation for the rational engineering of enzymes for programmable polymer deconstruction.

Amides↗

DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗

DOE OSTI2025

Dependent scattering and fractal microstructure determine the transparency of aerogel monoliths

This study reveals how dependent scattering and microstructure significantly affect electromagnetic wave propagation through aerogel monoliths, contributing to their transparency. Light scattering by particle ensembles is considered “dependent” when the scattering properties rely not only on particle size and optical constants but also on their spatial distribution, typically occurring when the average interparticle distance is small in comparison with the wavelength of incident radiation. Addressing dependent scattering requires solving Maxwell’s equations for complex heterogeneous structures, which is computationally demanding and usually limited to sample thicknesses on the same scale as the wavelength. This study combines computer-generated ambigel microstructures of fractal aggregates of polydisperse nanoparticles and the radiative transfer with reciprocal transaction method to predict the transmittance of thick ambigel slabs. Transmittance measurements of ambiently dried aerogel monoliths (ambigels) with porosities from about 50% to 90% closely matched the predicted values for their digital twins. However, ignoring dependent scattering or particle aggregation led to inaccurate predictions. This study validated the computational framework, and its findings offer insights for designing photonic metamaterials and analyzing their interactions with electromagnetic waves.

Yalcin, Refet A. (ORCID:0000000339973494)↗

DOE OSTI2025

A Bayesian approach to time-domain photonic Doppler velocimetry analysis

Photonic Doppler velocimetry (PDV) is an established technique for measuring the velocities of fast-moving surfaces in high-energy-density experiments. In the standard approach to PDV analysis, the short-time Fourier transform (STFT) is used to generate a spectrogram from which the velocity history of the target is inferred. The user chooses the form, duration, and separation of the window function. Here, in this study, we present a Bayesian approach to infer the velocity directly from the PDV oscilloscope trace, without using the spectrogram for analysis. This is clearly a difficult inference problem due to the highly periodic nature of the data, but we find that with carefully chosen prior distributions for the model parameters, we can accurately recover the injected velocity from synthetic data. We validate this method using PDV data collected at the STAR two-stage light gas gun at Sandia National Laboratories, recovering shock-front velocities in quartz that are consistent with those inferred using the STFT-based approach and are interpolated across regions of low signal-to-noise data. Although this method does not rely on the same user choices as the STFT, we caution that it can be prone to misspecification if the chosen model is not sufficient to capture the velocity behavior. Analysis using posterior predictive checks can be used to establish whether a better model is required, although more complex models come with additional computational cost, often taking more than several hours to converge when sampling the Bayesian posterior. We, therefore, recommend it be viewed as a complementary method to that of the STFT-based approach.

Allison, James R. [First Light Fusion Ltd., Yarnto↗

DOE OSTI2026

Federated learning for 2D synchrotron x-ray diffractometry: a cross-institutional approach for phase quantification of Ti–6Al–4V alloy

High-energy Two dimensional (2D) synchrotron x-ray diffractometry provides important insights into the atomistic structure and phase evolution of materials, yet traditional analysis methods remain complex, knowledge-intensive, and computationally demanding. Deep-learning models offer a powerful alternative for automating their analysis. Institutions that hold these datasets may be unwilling to share their data due to privacy and security policies, as well as the challenges associated with large-scale data transfer. As a result, models trained on local datasets often perform well only on their own data but exhibit bias and poor generalization across different instruments or facilities. To overcome these limitations, we explore federated learning (FL) for 2D synchrotron diffractograms, enabling collaborative model training without exchanging raw data. In this study, 2D synchrotron diffractograms of Ti–6Al–4V alloy collected from two independent facilities are used to train convolutional neural networks for predicting the β-phase volume fraction. Experimental results show that federated global models significantly outperform locally trained models in terms of generalization and achieve accuracy comparable to centralized trained models. These findings demonstrate the potential of FL to enable secure, cross-institutional collaboration and enhance the scalability of deep-learning-based materials characterization.

36 MATERIALS SCIENCE↗

DOE OSTI2025

TrioSim: A Lightweight Simulator for Large-Scale DNN Workloads on Multi-GPU Systems

Deep Neural Networks (DNNs) have become increasingly capable of performing tasks ranging from image recognition to content generation. The training and inference of DNNs heavily rely on GPUs, as GPUs' massively parallel architecture delivers extremely high computing capability. With the growing complexity of DNNs and the size of training datasets, training DNNs with a large number of GPUs is becoming a prevalent strategy. Researchers have been exploring how to design software and hardware systems for GPU farms to achieve the best utilization, efficiency, and DNN accuracy during training or inference. However, when designing and deploying such systems, designers usually rely on testing on physical hardware platforms equipped with many GPUs, incurring high costs that are almost prohibitive for system designers to test different configurations and designs, even for highly resourceful companies. While an alternative solution is to test on GPU simulators, they are often too slow for these l

Li, Ying [William & Mary, Williamsburg, VA, USA] (↗

DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗

DOE OSTI2024

Cold Plasma Measurements

We have continued the simulation campaign in support of our ongoing magnetospheric cold plasma research project. This project aims to develop the next-generation particle instruments to measure the properties of the cold particle populations in the Earth’s magnetosphere. For this purpose, simulations have been performed with a Particle-In-Cell (PIC) code called the Curvilinear PIC (CPIC). The code is formulated in curvilinear geometry and couples the standard PIC algorithm with algorithms for the generation and adaptation of the underlaying computational mesh. It conforms to complex objects like spacecraft and it can place more grid points in regions where higher resolution is needed. The code also features a scalable solver based on the multigrid algorithm and it is fully parallelized via domain decomposition and MPI.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

An Efficient Storage-Driven Machine Learning Model for Performance in the Era of Multimodal Scientific Data

Scientific workflows are increasingly relying on machine learning (ML), simulation, and hybrid techniques to predict, understand, and optimize the behavior of complex experiments. High-performance computing has greatly improved researchers’ ability to acquire diverse data modalities in these workflows. Recent studies suggest that the performance of machine learning models can be improved by integrating data from various sources. Unfortunately, these workloads pose unprecedent pressure on the network storage to meet the demands associated with accessing these multimodal data. To mitigate the impact of intensive IO, we propose a solution that utilizes a multi-tier High-Performance Computing (HPC) distributed storage and data processing framework, placing computation where the data resides for better performance. By adopting this project, the scientific community will gain new opportunities to explore multimodal storage-driven possibilities, integrating multiple scientific data sources with advanced streaming frameworks. Additionally, our framework effectively utilizes computing resources and bridges the gaps identified by HPC experts. Our proposed approach tackles scalability and persistence challenges by leveraging native persistency, which has posed difficulties in traditional approaches. Furthermore, we seek to enhance fault-tolerance and load-balance of computations by leveraging real-time streaming in diverse scientific computing environments, thereby propelling advanced scientific computing research into the next generation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Optimizing Distributed Training on Frontier for Large Language Models

Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Loss scaling studies have demonstrated the superior performance of larger LLMs compared to their smaller counterparts. Nevertheless, training LLMs with billions of parameters poses significant challenges and requires considerable computational resources. For example, training a one trillion parameter GPT-style model on 20 trillion tokens requires a staggering 120 million exaflops. This research explores efficient distributed training strategies to extract this computation from Frontier, the world's first exascale supercomputer. We enable and investigate various model and data parallel training techniques, such as tensor parallelism, pipeline parallelism, and sharded data parallelism, to facilitate training a trillion-parameter model on Frontier. We empirically assess these techniques and their associated parameters to determine their impact on memory footprint, communication latency, and GPU's computational efficiency. We analyze the complex interplay among these techniques and find a strategy to combine them to achieve high throughput through hyperparameter tuning. We have identified efficient strategies for training large LLMs of varying sizes through empirical analysis and hyperparameter tuning. For 22 Billion, 175 Billion, and 1 Trillion parameters, we achieved GPU throughputs of 38.38%, 36.14%, and 31.96%, respectively. For the training of the 175 Billion parameter model and the 1 Trillion parameter model, we achieved 100% weak scaling efficiency on 1024 and 3072 Mi250X GPUs, respectively. We also achieved strong scaling efficiencies of 89% and 87% for these two models. We trained these models only tens of iterations instead of training till completion.

Yin, Junqi↗

DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗