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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Disentangling the intertwined orders in a magnetic kagome metal

Intertwined orders appear when multiple orders are strongly interacting, and kagome metals have emerged as new platforms to explore exotic phases. FeGe has been found to develop a charge density wave (CDW) order within magnetic phase, suggesting an intricate interplay of the lattice, charge, and spin degrees of freedom. Recently, postgrowth annealing has been proposed to tune the CDW order from long-range to complete suppression, offering a tuning knob for the CDW order. Here, by comparing the electronic structures of FeGe subjected to different annealing conditions and distinct CDW properties, we report spectral evolution associated with the lattice and spin degrees of freedom. We find band evolution linked to a spin density wave (SDW) order present in both samples with and without CDW order, and another evolution connected to the lattice distortions that onset with the long-range CDW order and revert with the SDW order. Our results reveal a rare competitive cooperation of the lattice, spin, and charge in FeGe.

Oh, Ji Seop↗
DOE OSTI2025

Supramolecular bending and twisting in the hierarchical self-assembly of monodisperse mesogenic oligomers

Understanding how different forms of supramolecular curvature arise during assembly is crucial to designing and tuning the microstructure of hierarchically self-assembled materials. Here, we show that in crystalline phases of mesogenic oligomers, the oligomer length is a critical parameter that determines the type of curvature (Gaussian or cylindrical) exhibited by the self-assembled structures. We use iterative exponential growth to synthesize monodisperse mesogenic oligomers ranging from dimer to octamer. By analyzing their phase behavior and microstructure, we elucidate how length-dependent thermodynamic and kinetic effects tune their hierarchical degree of ordering. The oligomers’ length-dependent crystalline order drives the formation of scrolled sheets in shorter oligomers and twisted ribbons in longer oligomers. These studies highlight how oligomer length interplays with mesogen geometry and crystalline packing to drive self-assembly, introducing oligomer length as a powerful design parameter toward tailored applications of mesogenic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quieting a noisy antenna reproduces photosynthetic light-harvesting spectra

Photosynthesis achieves near unity light-harvesting quantum efficiency yet it remains unknown whether there exists a fundamental organizing principle giving rise to robust light harvesting in the presence of dynamic light conditions and noisy physiological environments. Here, we present a noise-canceling network model that relates noisy physiological conditions, power conversion efficiency, and the resulting absorption spectra of photosynthetic organisms. Using light conditions in full solar exposure, light filtered by oxygenic phototrophs, and light filtered under seawater, we derived optimal absorption characteristics for efficient solar power conversion. We show how light-harvesting antennae can be tuned to maximize power conversion efficiency by minimizing excitation noise, thus providing a unified theoretical basis for the observed wavelength dependence of absorption in green plants, purple bacteria, and green sulfur bacteria.

Science & Technology - Other Topics↗
DOE OSTI2025

Identification of short-range ordering motifs in semiconductors

Chemical short-range ordering is expected to be a key factor for tuning the electronic structure of semiconductors. However, experimental evidence of short-range ordering is still lacking due to the challenge of characterizing atomic-scale ordering motifs. Here, we determined the presence of short-range order in a ternary GeSiSn semiconductor system using advanced energy-filtered four-dimensional scanning transmission electron microscopy and large-scale atomistic models generated by a machine learning neuroevolution potential of first-principles accuracy. This approach revealed preferred ordering of different atomic species with the dominant occurrence of Si–Ge–Sn triplets. Our findings not only confirmed the presence of short-range order but also directly revealed the actual atomic structure, demonstrating the potential for informed atomic order–based band engineering as a third degree of freedom beyond composition and strain tuning.

Vogl, Lilian M. [University of California Berkeley↗
DOE OSTI2020

Linking the Dynamic Response of the Carbon Dioxide-Concentrating Mechanism to Carbon Assimilation Behavior in Fremyella diplosiphon

ABSTRACT Cyanobacteria use a carbon dioxide (CO 2 )-concentrating mechanism (CCM) that enhances their carbon fixation efficiency and is regulated by many environmental factors that impact photosynthesis, including carbon availability, light levels, and nutrient access. Efforts to connect the regulation of the CCM by these factors to functional effects on carbon assimilation rates have been complicated by the aqueous nature of cyanobacteria. Here, we describe the use of cyanobacteria in a semiwet state on glass fiber filtration discs—cyanobacterial discs—to establish dynamic carbon assimilation behavior using gas exchange analysis. In combination with quantitative PCR (qPCR) and transmission electron microscopy (TEM) analyses, we linked the regulation of CCM components to corresponding carbon assimilation behavior in the freshwater, filamentous cyanobacterium Fremyella diplosiphon . Inorganic carbon (C i ) levels, light quantity, and light quality have all been shown to influence carbon assimilation behavior in F. diplosiphon . Our results suggest a biphasic model of cyanobacterial carbon fixation. While behavior at low levels of CO 2 is driven mainly by the C i uptake ability of the cyanobacterium, at higher CO 2 levels, carbon assimilation behavior is multifaceted and depends on C i availability, carboxysome morphology, linear electron flow, and cell shape. Carbon response curves (CRCs) generated via gas exchange analysis enable rapid examination of CO 2 assimilation behavior in cyanobacteria and can be used for cells grown under distinct conditions to provide insight into how CO 2 assimilation correlates with the regulation of critical cellular functions, such as the environmental control of the CCM and downstream photosynthetic capacity. IMPORTANCE Environmental regulation of photosynthesis in cyanobacteria enhances organismal fitness, light capture, and associated carbon fixation under dynamic conditions. Concentration of carbon dioxide (CO 2 ) near the carbon-fixing enzyme RubisCO occurs via the CO 2 -concentrating mechanism (CCM). The CCM is also tuned in response to carbon availability, light quality or levels, or nutrient access—cues that also impact photosynthesis. We adapted dynamic gas exchange methods generally used with plants to investigate environmental regulation of the CCM and carbon fixation capacity using glass fiber-filtered cells of the cyanobacterium Fremyella diplosiphon . We describe a breakthrough in measuring real-time carbon uptake and associated assimilation capacity for cells grown in distinct conditions (i.e., light quality, light quantity, or carbon status). These measurements demonstrate that the CCM modulates carbon uptake and assimilation under low-C i conditions and that light-dependent regulation of pigmentation, cell shape, and downstream stages of carbon fixation are critical for tuning carbon uptake and assimilation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Reflections on Cyanobacterial Chromatic Acclimation: Exploring the Molecular Bases of Organismal Acclimation and Motivation for Rethinking the Promotion of Equity in STEM

Cyanobacteria are photosynthetic organisms that exhibit characteristic acclimation and developmental responses to dynamic changes in the external light environment. Photomorphogenesis is the tuning of cellular physiology, development, morphology, and metabolism in response to external light cues. The tuning of photosynthetic pigmentation, carbon fixation capacity, and cellular and filament morphologies to changes in the prevalent wavelengths and abundance of light have been investigated to understand the regulation and fitness implications of different aspects of cyanobacterial photomorphogenesis. Chromatic acclimation (CA) is the most common form of photomorphogenesis that has been explored in cyanobacteria. Multiple types of CA in cyanobacteria have been reported, and insights gained into the regulatory pathways and networks controlling some of these CA types. I examine the recent expansion of CA types that occur in nature and provide an overview of known regulatory factors involved in distinct aspects of cyanobacterial photomorphogenesis. Additionally, I explore lessons for cultivating success in scientific communities that can be drawn from a reflection on existing knowledge of and approaches to studying CA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Learning Robust Marking Policies for Adaptive Mesh Refinement

Here in this work, we revisit the marking decisions made in the standard adaptive finite element method (AFEM). Experience shows that a naïve marking policy leads to inefficient use of computational resources for adaptive mesh refinement (AMR). Consequently, using AMR in practice often involves ad-hoc or time-consuming offline parameter tuning to set appropriate parameters for the marking subroutine. To address these practical concerns, we recast AMR as a Markov decision process in which refinement parameters can be selected on-the-fly at run time, without the need for pre-tuning by expert users. In this new paradigm, the refinement parameters are also chosen adaptively via a marking policy that can be optimized using methods from reinforcement learning. We use the Poisson equation to demonstrate our techniques on h- and hp-refinement benchmark problems, and our experiments suggest that superior marking policies remain undiscovered for many classical AFEM applications. Furthermore, an unexpected observation from this work is that marking policies trained on one family of PDEs are sometimes robust enough to perform well on problems far outside the training family. For illustration, we show that a simple hp-refinement policy trained on 2D domains with only a single re-entrant corner can be deployed on far more complicated 2D domains, and even 3D domains, without significant performance loss. For reproduction and broader adoption, we accompany this work with an open-source implementation of our methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Surrogate-Based Autotuning for Randomized Sketching Algorithms in Regression Problems

Algorithms from Randomized Numerical Linear Algebra (RandNLA) are known to be effective in handling high-dimensional computational problems, providing high-quality empirical performance as well as strong probabilistic guarantees. However, their practical application is complicated by the fact that the user needs to set various algorithm-specific tuning parameters which are different from those used in traditional NLA. This paper demonstrates how a surrogate-based autotuning approach can be used to address fundamental problems of parameter selection in RandNLA algorithms. In particular, we provide a detailed investigation of surrogate-based autotuning for sketch-and-precondition (SAP)-based randomized least squares methods, which have been one of the great success stories in modern RandNLA. Empirical results show that our surrogate-based autotuning approach can achieve near-optimal performance with much less tuning cost than a random search (up to about 7.6x fewer trials of different parameter configurations). Moreover, while our experiments focus on least squares, our results demonstrate a general-purpose autotuning pipeline applicable to any kind of RandNLA algorithm.

Cho, Younghyun↗
DOE OSTI2025

Complex deuteron NMR signals

To determine the spin polarization of deuterons, nuclear magnetic resonance (NMR) is used. This is necessary for polarized targets, such as for the upcoming A zz and b 1 experiment at Jefferson Lab. NMR measures the impedance of a solenoid around a deuterated sample. Although the impedance is a complex value, conventionally only the real part of the impedance has been used for this purpose. However, often the tune is not precisely real, meaning the signal has at least some small imaginary portion. This conventionally has been dealt with by an offset parameter, such as Dulya’s false asymmetry method. For vector polarization, this suffices, as the tuning is factored into the overall error of the results, and for a small phase angle doesn’t make much of a difference. However, for tensor polarization, the exact lineshape of the signal is quite significant, and treating the impedance as complex during analysis removes the need for an offset parameter. As a result, it also provides more accurate results, as the conventional false asymmetry method over- or underestimates polarization, depending on the sign of the phase angle.

McClellan, Michael [University of New Hampshire, D↗
DOE OSTI2021

Recent highlights from GENIE v3

Abstract The release of GENIE v3.0.0 was a major milestone in the long history of the GENIE project, delivering several alternative comprehensive neutrino interaction models, improved charged-lepton scattering simulations, a range of beyond the Standard Model simulation capabilities, improved experimental interfaces, expanded core framework capabilities, and advanced new frameworks for the global analysis of neutrino scattering data and tuning of neutrino interaction models. Steady progress continued following the release of GENIE v3.0.0. New tools and a large number of new physics models, comprehensive model configurations, and tunes have been made publicly available and planned for release in v3.2.0. This article highlights some of the most recent technical and physics developments in the GENIE v3 series.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

I/O Access Patterns in HPC Applications: A 360-Degree Survey

The high-performance computing I/O stack has been complex due to multiple software layers, the inter-dependencies among these layers, and the different performance tuning options for each layer. In this complex stack, the definition of an “I/O access pattern” has been reappropriated to describe what an application is doing to write or read data from the perspective of different layers of the stack, often comprising a different set of features. It has become common to have to redefine what is meant when discussing a pattern in every new study, as no assumption can be made. This survey aims to propose a baseline taxonomy, harnessing the I/O community’s knowledge over the past 20 years. This definition can serve as a common ground for high-performance computing I/O researchers and developers to apply known I/O tuning strategies and design new strategies for improving I/O performance. We seek to summarize and bring a consensus to the multiple ways to describe a pattern based on common features already used by the community over the years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Insights from Optimizing HPL Performance on Exascale Systems: A Comparative Analysis of Panel Factorization

High performance LINPACK (HPL) remains the primary benchmark for evaluating supercomputing performance. It includes many parts with substantial internal complexity, and its performance is affected by a large number of parameters that interact in ways that are difficult to predict on large-scale heterogeneous supercomputer systems. We present a comprehensive performance analysis of HPL on Frontier, the world’s first exascale supercomputer, which achieved HPL performance of 1.35 exaflops. Through empirical parameter tuning, detailed modeling, and comparative evaluation, we uncover critical performance insights, share lessons learned, and outline best practices for effective parameter tuning on exascale systems. We introduce and evaluate two novel PDFACT strategies: a dedicated-thread (DT) variant and a GPU-based variant (GPUPDFACT) implementation using HIP cooperative groups, demonstrating that GPU-based factorization outperforms conventional CPU-based PDFACT on Frontier’s architecture. Our findings establish key performance factors for HPL on exascale systems and offer valuable guidance for future high-performance computing and benchmarking efforts.

Lu, Hao [ORNL] (ORCID:000000018941870X)↗
DOE OSTI2025

Agentic AI vs ML-Based Autotuning: A Comparative Study for Loop Reordering Optimization

High Performance Computing (HPC) applications rely heavily on code optimizations to achieve good performance on modern CPU and GPU architectures. Traditional Machine Learning auto-tuning approaches have demonstrated success in exploring high-dimensional spaces, but they often require expensive compile-run evaluations and lack adaptability for large HPC applications. The recent advances in Large Language Models (LLMs) and Agentic AI systems raise intriguing questions about the potential of these approaches to address specific optimization methodologies. This work aims to answer an essential question for the HPC community: “How Agentic AI Systems Compare to Traditional ML Autotuning Techniques?” To address this question, we present a comparative analysis between a traditional ML-based optimization approach and an Agentic AI system, evaluating their respective capabilities and limitations for loop-level optimization. In addition, we introduced a new Agentic AI system named LoopGen-AI using three different Large Language Models: GPT-4.1, Claude 4.0, and Gemini 2.5. A key finding is that LoopGen-AI achieves competitive per-formance with only a few program runs, the reasoning logs from the agents revealed that their decisions rely heavily on the combination of semantic understanding of the target kernel with dynamic feedback from the environment, highlighting a promising new dimension in performance tuning. In contrast, ML-based autotuners focus on statistical exploration, and require orders of magnitude more runs to reach peak performance. Additionally, our analysis shows that prompt engineering, particularly using Persona + Context Manager patterns, significantly impacts the effectiveness of Agentic AI. Our results indicate that while Agentic AI systems are not yet a complete replacement for ML-based autotuners, it can effectively complement traditional methods.

Rosas, Miguel Romero↗
DOE OSTI2026

BrickQA: Bridging the Semantic Gap in Building Operations with Dynamic Graph Exploration

While standardized ontologies like the Brick schema address data heterogeneity in Building Automation Systems (BAS), accessing this semantic data remains a challenge as domain experts often lack the expertise to formulate complex SPARQL queries. To bridge this gap, we present BrickQA, a Large Language Model (LLM)-based framework that translates natural language into executable SPARQL queries through structured query decomposition, dynamic schema exploration, and inline validation. BrickQA utilizes an iterative reasoning agent to actively navigate graph topology through dynamic exploration actions without requiring exhaustive context injection or model fine-tuning. This approach effectively mitigates hallucinations, particularly in large-scale building knowledge graphs. Empirical evaluation on BuildingQA, a standardized benchmark, demonstrates that BrickQA significantly outperforms ReAct baselines, delivering a 0.291–0.355 absolute F1 improvement while achieving 3 × –12.7 × higher token cost-efficiency. Beyond these metrics, the framework maintains structural fidelity across heterogeneous buildings and remains resilient to ambiguous queries without requiring site-specific fine-tuning. Furthermore, a case study on operational analytics validates the framework’s capability to handle temporal and aggregation constraints, effectively transforming abstract semantic models into actionable facility management insights.1

Ko, Yun-Dam↗
DOE OSTI2026

ChatMPI: LLM-Driven MPI Code Generation for HPC Workloads

The Message Passing Interface (MPI) standard plays a crucial role in enabling scientific applications for parallel computing and is an essential component in high-performance computing (HPC). However, implementing MPI code manually—especially applying a proper domain decomposition and communication pattern—is a challenging and error-prone task. We present ChatMPI, an AI assistant for MPI parallelization of sequential C codes. In our analysis, we focus on testing six essential HPC workloads, which are based on Basic Linear Algebra Subprograms levels 1, 2, and 3 as well as sparse, stencil, and iterative operations. We analyze the process of creating ChatMPI by using the ChatHPC library. This lightweight large language model (LLM)–based infrastructure enables HPC experts to efficiently create and supervise trustworthy AI capabilities for critical HPC software tasks. We study the data required for training (fine-tuning) ChatMPI to generate parallel codes that not only use MPI syntax correctly but also apply HPC techniques to reduce memory communication and maximize performance by using proper work decomposition. With a relatively small training dataset composed of a few dozen prompts and fewer than 15 minutes of fine-tuning on one node equipped with two NVIDIA H100 GPUs, ChatMPI elevates trustworthiness for MPI code generation of current LLMs (e.g., Code Llama, ChatGPT-4o and ChatGPT 5). Additionally, we evaluate the performance of the MPI codes generated by ChatMPI in comparison with the ones generated by ChatGPT-4o and ChatGPT-5. The codes generated by ChatMPI provide up to a 4 × boost in performance by using better problem decomposition, communication patterns, and HPC techniques (e.g., communication avoiding).

Valero Lara, Pedro [ORNL] (ORCID:0000000214794310)↗
DOE OSTI2021

Layered Double Perovskites

Successful strategies for the design of crystalline materials with useful function are frequently based on the systematic tuning of chemical composition within a given structural family. Perovskites with the formula ABX 3 , perhaps the best-known example of such a family, have a vast range of elements on A, B, and X sites, which are associated with a similarly vast range of functionality. Layered double perovskites (LDPs), a subset of this family, are obtained by suitable slicing and restacking of the perovskite structure, with the additional design feature of ordered cations and/or anions. In addition to inorganic LDPs, we also discuss hybrid (organic-inorganic) LDPs here, where the A-site cation is a protonated organic amine. Several examples of inorganic LDPs are presented with a discussion of their ferroic, magnetic, and optical properties. The emerging area of hybrid LDPs is particularly rich and is leading to exciting discoveries of new compounds with unique structures and fascinating optoelectronic properties. We provide context for what is important to consider when designing new materials and conclude with a discussion of future opportunities in the broad LDP area.

Materials Science↗
DOE OSTI2021

Ionomer Optimization for Water Uptake and Swelling in Anion Exchange Membrane Electrolyzer: Hydrogen Evolution Electrode

Green hydrogen produced through anion exchange membrane water electrolysis is a promising, low-cost chemical storage solution for intermittent renewable energy sources. Low-temperature electrolysis using anion exchange membranes (AEM) combines the benefits of established water electrolysis technologies based on alkaline electrolysis and proton exchange membrane electrolysis. The anion conductive ionomers (ACI) used in the AEM electrolyzer (AEMEL) electrodes has been investigated. The ACI serves two primary purposes: (i) facilitate hydroxide conduction between the catalyst and bulk electrolyte and (ii) bind the catalyst to the porous transport layer and membrane. High ion exchange capacity (IEC) ACIs are desired, however, high IEC can cause excessive water uptake (WU) and detrimental ACI swelling. Proper water management is a key factor in obtaining maximum performance in AEM-based devices. In this study, a series of poly(norbornene)-based ACIs were synthesized and deployed in hydrogen evolving AEMEL cathode electrodes. A balance between ionic conductivity, WU and ionomer swelling was achieved in the ACI by varying the IEC and degree of polymer cross-linking. It was found that higher IEC ACIs with light crosslinking are preferred in the HER electrode. Furthermore, such a configuration fine-tuned the WU and ionomer swelling to achieve optimum cell performance and reduce cell operating voltages.

08 HYDROGEN↗
DOE OSTI2020

Parameter Optimization Toolbox for NS-3 network optimization, NS-3 Parameter Optimization Framework [SWR-18-60]

This simulation-based parameter optimization framework is proposed to tune parameters of different types of communication networks using ns-3 to achieve the optimal network performance. It consists of three main components: an ns-3 packet reporting module; a sampler running simulations with all possible parameter sets for the input parameter variables by using a parallel executor at each generation; and a hybrid optimization algorithm for tuning configurable parameters of hybrid designs and application parameter variables. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient descent function to quickly achieve an approximate globally optimum solution. This software is designed to be used in a multi-core processing Linux environment and run over a long duration of time. The execution time varies depending mainly upon the nature of the ns-3 configuration being simulated. This software includes a custom ns-3 QoS measurement application which must be included with the ns-3 source code during installation of the software.

Hasandka, Adarsh↗