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DOE OSTI2024

Integrating ORNL’s HPC and Neutron Facilities with a Performance-Portable CPU/GPU Ecosystem

We explore the development of a performance-portable CPU/GPU ecosystem to integrate two of the US Department of Energy’s (DOE’s) largest scientific instruments, the Oak Ridge Leadership Computing facility and the Spallation Neutron Source (SNS), both of which are housed at Oak Ridge National Laboratory. We select a relevant data reduction workflow use-case to obtain the differential scattering cross-section from data collected by SNS’s CORELLI and TOPAZ instruments. We compare the current CPU-only production implementation using the Garnet Python multiprocess package based on the Mantid C++ framework against our proposed CPU/GPU implementation that uses the LLVM-based, just-in-time Julia scientific language and the JACC.jl performance-portable package. Two proxy apps were developed: (i) an app for extracting relevant Mantid kernels (MDNorm) in C++ and (ii) the Julia MiniVATES.jl miniapp. We present performance results for NVIDIA A100 and AMD MI100 GPUs and AMD EPYC 7513 and 7662 CPUs. The results provide insights for future generations of data reduction software that can embrace performance portability for an integrated research infrastructure across DOE’s experimental and computational facilities.

Hahn, Steven↗

DOE OSTI2024

A portable application framework for energy management and information systems (EMIS) solutions using Brick semantic schema

This paper introduces a portable framework for developing, scaling and maintaining energy management and information systems (EMIS) applications using an ontology-based approach. Key contributions include an interoperable layer based on Brick schema, the formalization of application constraints pertaining metadata and data requirements, and a field demonstration. The framework allows for querying metadata models, fetching data, preprocessing, and analyzing data, thereby offering a modular and flexible workflow for application development. Its effectiveness is demonstrated through a case study involving the development and implementation of a data-driven anomaly detection tool for the photovoltaic systems installed at the Politecnico di Torino, Italy. During eight months of testing, the framework was used to tackle practical challenges including: (i) developing a machine learning-based anomaly detection pipeline, (ii) replacing data-driven models during operation, (iii) optimizing model deployment and retraining, (iv) handling critical changes in variable naming conventions and sensor availability (v) extending the pipeline from one system to additional ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs

Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today’s heterogeneous computing systems with GPUs.In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are four-fold: (1) We carefully optimize the bitplane encoding and lossless encoding, two key stages in progressive methods, to achieve high performance on GPUs; (2) We propose pipeline optimization and incorporate it with data refactoring and progressive retrieval workflows to further enhance the performance for large data process; (3) We leverage our framework to enable high-performance data retrieval with guaranteed error control for common Quantities of Interest; (4) We evaluate HP-MDR and compare it with state of the arts using five real-world datasets. Experimental results demonstrate that HP-MDR delivers an average 13.68 × and 6.31 × throughput in data refactoring and progressive retrieval tasks, respectively. It also leads to 11.22 × throughput for recomposing required data representations under Quantity-of-Interest error control and 6.04 × performance for the corresponding end-to-end data retrieval, when compared with state-of-the-art solutions.

Li, Yanliang [University of Oregon]↗

DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

NASA NTRSDate not supplied

Culture-Independent Fungal Profiling for the International Space Station using Nanopore Sequencing: Method Development

Microbial monitoring of the International Space Station (ISS) environment is a crew health requirement that encompasses both bacterial and fungal identification. To achieve this currently, culture-based methods are used for sample collection, and these samples must be returned to the laboratory for analysis. The use of culture and the need for sample return to Earth results in a bias toward culturable organisms and causes a significant delay between sample collection and delivery of final data (weeks to months), respectively. Recently, advancements in molecular technology have aided a broad range of applications, including medical, industrial, and basic sciences. Additionally, increases in portability and ease-of-use of molecular platforms have provided point-of-use capabilities demonstrated by the miniPCR thermal cycler (miniPCR bio) and the MinION sequencer (Oxford Nanopore Technologies). Together, these devices have been applied to, and validated for, the identification of bacteria onboard the ISS. Building on this work, we have developed a spaceflight-compatible fungal workflow. Molecular-based fungal analysis is complicated by low biomass, difficult-to-lyse spores, debate regarding the region for taxonomic assignment, and the lack of bioinformatic pipelines and reference databases. To overcome these difficulties, primers yielding an ~ 2 Kb amplicon were validated against a wide range of ISS fungal isolates. The current spaceflight library preparation was substantially optimized, a bioinformatic pipeline was created, and refinements to the UNITE database were implemented. To compare this optimized method to the current culture-based standard, 30 sample sets (60 total swabs, two swabs held in tandem) were evaluated. Parallel fungal profiles were obtained between the two methods, with the culture-independent method revealing increased diversity. The addition of this method to the already established bacterial process fulfills the crew health identification requirement. Moreover, the implementation of this method onboard ISS will enhance our understanding of its unique fungal microbiome.

Hang N. Nguyen↗

NASA NTRS2011

The Challenges of Releasing Human Data for Analysis

The NASA Johnson Space Center s (NASA JSC) Committee for the Protection of Human Subjects (CPHS) recently approved the formation of two human data repositories: the Lifetime Surveillance of Astronaut Health Repository (LSAH-R) for clinical data and the Life Sciences Data Archive Repository (LSDA-R) for research data. The establishment of these repositories forms the foundation for the release of data and information beyond the scope for which the data was originally collected. The release of clinical and research data and information is primarily managed by two NASA groups: the Evidence Base Working Group (EBWG), consisting of members of both repositories, and the LSAH Policy Board. The goal of unifying these repositories and their processes is to provide a mutually supportive approach to handling medical and research data, to enhance the use of medical and research data to reduce risk, and to promote the understanding of space physiology, countermeasures and other mitigation strategies. Over the past year, both repositories have received over 100 data and information requests from a wide variety of requesters. The disposition of these requests has highlighted the challenges faced when attempting to make data collected on a unique set of subjects available beyond the original intent for which the data were collected. As the EBWG works through each request, many considerations must be factored into account when deciding what data can be shared and how - from the Privacy Act of 1974 and the Health Insurance Portability and Accountability Act (HIPAA), to NASA s Health Information Management System (10HIMS) and Human Experimental and Research Data Records (10HERD) access requirements. Additional considerations include the presence of the data in the repositories and vetting requesters for legitimacy of their use of the data. Additionally, fair access must be ensured for intramural, as well as extramural investigators. All of this must be considered in the formulation of the charters, policies and workflows for the human data repositories at NASA.

Fitts, Mary↗

NASA NTRSDate not supplied

Modeling and Simulation Efforts to Support Improved Comfort in ARGOS

BACKGROUND: The Active Response Gravity Offload System (ARGOS) provides an analog environment for extravehicular activity (EVA) testing and training. Discomfort has been observed during longer suited test sessions. While the subject’s core is offloaded during surface EVA evaluations, his/her arms experience full Earth gravity and can become overly fatigued, especially during suited tests which involve reaching and prolonged arm extensions. A device (ARGOS Negation of Gravitational Effects on the Limbs: ANGEL) to offload the weight of the arms and suit sleeves is being developed by JSC’s Flight Systems Branch, and here we present preliminary modeling of that device using the open-source biomechanical tool OpenSim [1,2] with an in-house developed plugin. We analyze a series of motions performed by a single shirt-sleeved subject with goals of characterizing the device, validating the model, and predicting whether reduced gravity conditions (i.e., lunar gravity (Lg) or Martian gravity (Mg)) can be accurately simulated with the device, as well as providing comfort to the ARGOS user. METHODS AND RESULTS: To model the offload device, we augment the OpenSim human model topology with the offload mechanism components and joints, using CAD models to represent the mechanism graphically. The joint angles of the device are either obtained from (1) inverse kinematics (IK) using motion capture markers on the various components of the device or (2) calculated in the OpenSim plugin by modeling how the components configure themselves under the offloading spring tension given a particular IK-derived arm position. Given the joint angles of the device, the resulting force on the arm is computed by the plugin and applied as an external load in inverse dynamics (ID) in order to enable study of overall shoulder joint torques as well as offload achieved. We verify the calculated joint angles by using the inverse kinematic data and the forces from manual measurements of the spring both independently and integrated within the device. We found that calculated joint angles generally represent the angles measured and computed with IK, supporting a possible analysis workflow inputting human motion data and observing system behavior under varied design parameters. In two different device configurations in which the maximum applied force was 131 N, our current model accurately captured force with a difference of 2-3 N from measured loads. Though our initial test was performed with a shirt-sleeve subject, arm weights were added to emulate the weight of the suit sleeve and the subject was positioned in a test stand with a Mark-III Hard Upper Torso (HUT) and Portable Life Support System (PLSS) mockup. Arm range of motion tasks were performed outside of the HUT, inside the HUT, and inside the HUT while using the device. A variety of other upper body tasks were completed as well. In summary, we have developed a model to investigate and verify an upper limb offload device currently in development. We believe this model will be a valuable tool not only for device characterization but also to predict proper configurations to simulate Lg or Mg conditions, investigate range of motion concerns, predict limitations such as internal collisions and contacts, and inform future design improvements.

L B Nilsson↗

DOE OSTI2026

Reining in an Agentic Harness for High Energy Physics

Agentic systems now address tasks across theoretical, phenomenological, and experimental high energy physics (HEP), but their scientific capabilities remain difficult to reuse across different large language models, providers, and harnesses. We argue that stable parts of these workflows should be promoted into versioned scientific operations and exposed through common protocols. Existing general-purpose harnesses can then be specialized for HEP through task-specific sets of tools and skills, while community-maintained registries would make these capabilities discoverable and citable. We identify mismatches in conventions, assumptions, and domains of validity among independently developed operations as a potential obstacle to their composition, and discuss machine-readable scientific contracts as one possible solution. These design principles and evaluation guidelines provide a near-term path toward a portable and community-maintained agentic harness for HEP.

Menzo, Tony [Alabama U.; Fermilab] (ORCID:00000002↗

DOE OSTI2025

PDF Entity Annotation Tool (PEAT)

While different text mining approaches – including the use of Artificial Intelligence (AI) and other machine based methods - continue to expand at a rapid pace, the tools used by researchers to create the labeled datasets required for training, modeling, and evaluation remain rudimentary. Labeled datasets contain the target attributes the machine is going to learn; for example, training an algorithm to delineate between images of a car or truck would generally require a set of images with a quantitative description of the underlying features of each vehicle type. Development of labeled textual data that can be used to build natural language machine learning models for scientific literature is not currently integrated into existing manual workflows used by domain experts. Published literature is rich with important information, such as different types of embedded text, plots, and tables that can all be used as inputs to train ML/natural language processing (NLP) models, when extracted and prepared in machine readable formats. Currently, both normalized data extraction of use to domain experts and extraction to support development of ML/NLP models are labor intensive and cumbersome manual processes. Automatic extraction of data and information from formats such as PDFs that are optimized for layout and human readability, not machine readability. The PDF (Portable Document Format) Entity Annotation Tool (PEAT) was developed with the goal of allowing users to annotate publications within their current print format, while also allowing those annotations to be captured in a machine-readable format. One of the main issues with traditional annotation tools is that they require transforming the PDF into plain text to facilitate the annotation process. While doing so lessens the technical challenges of annotating data, the user loses all structure and provenance that was inherent in the underlying PDF. Also, textual data extraction from PDFs can be an error prone process. Challenges include identifying sequential blocks of text and a multitude of document formats (multiple columns, font encodings, etc.). As a result of these challenges, using existing tools for development of NLP/ML models directly from PDFs is difficult because the generated outputs are not interoperable. We created a system that allows annotations to be completed on the original PDF document structure, with no plain text extraction. The result is an application that allows for easier and more accurate annotations. In addition, by including a feature that grants the user the ability to easily create a schema, we have developed a system that can be used to annotate text for different domain-centric schemas of relevance to subject matter experts. Different knowledge domains require distinct schemas and annotation tags to support machine learning.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Developing multi-gene CRISPRa/i programs to accelerate DBTL cycles in ABF hosts engineered for chemical production

This project developed and implemented a modular CRISPR activation and interference (CRISPRa/i) platform to accelerate strain optimization and pathway development for industrially relevant microbial hosts. By integrating multiplexed transcriptional perturbation tools with data-driven Design–Build–Test–Learn (DBTL) workflows, the team achieved reductions in cycle time and enhanced production of industrial aromatics, particularly 4-aminocinnamic acid (4-ACA), in Pseudomonas putida. Key accomplishments included: ● Development of a robust, tunable CRISPRa/i system in P. putida that enabled efficient multi-target gene regulation via guide RNA (gRNA) programs ● Completion of two full DBTL cycles, guided by machine learning (ML) models trained on transcriptomic and performance data, reducing engineering time by over 30% ● Optimization of multi-gene regulatory programs to balance expression of host and pathway modules, improve 4-ACA titers, and resolve metabolic bottlenecks ● Demonstration of system portability through a limited proof-of-concept extension in Acinetobacter baylyi, underscoring the generalizability of the approach ● Evaluation of strain performance on lignocellulosic biomass-derived substrates, demonstrating the feasibility of converting renewable carbon into aromatic building blocks These results illustrate the feasibility of applying ML-guided CRISPRa/i perturbation strategies to accelerate strain development in complex microbial systems. The resulting tools and datasets contribute to DOE objectives by improving platform predictability, reducing development costs, and enabling broader access to sustainable, economically viable bioproduction technologies.

09 BIOMASS FUELS↗

DOE OSTI2025

2025 Advances in NekRS: Supporting improved performance for nuclear applications

This report presents several 2025 advancements in NekRS, a high-fidelity spectral element CFD code developed at Argonne National Laboratory to support the NEAMS thermal-hydraulics program. The forthcoming v25 release consolidates several of these advances, adding new features for portability across heterogeneous GPU architectures, real-time in situ visualization, improved turbulence modeling, and conjugate heat transfer coupling. Over the past year, NekRS has demonstrated strong scalability and performance on DOE’s leading exascale platforms, including Aurora and Frontier, confirming its readiness for some of the largest and most complex simulations attempted to date. These achievements provide a powerful new platform for high-fidelity data generation, which in turn supports the development and validation of advanced closure models critical for reactor safety and design. Significant algorithmic innovations have also been introduced. A new global runtime h-refinement capability simplifies workflows by reducing mesh preparation burdens and enabling coarse-to-fine restarts. Building on this, a novel multigrid strategy was implemented to accelerate pressure and transport solves at scale, addressing long-standing bottlenecks in exascale CFD. Together, these developments improve both the efficiency and accessibility of high-fidelity simulations for reactor-relevant problems. Collectively, these enhancements represent a major step forward in simulation technology, positioning NekRS as a cornerstone of NEAMS efforts to enable accurate, efficient, and scalable high-fidelity analysis of advanced nuclear systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

ExaCA v2.0: A versatile, scalable, and performance portable cellular automata application for additive manufacturing solidification

The previously established ExaCA software for performance portable alloy grain structure simulation has been updated to better represent the solidification behavior during complex alloy processing conditions, such as those encountered during metal additive manufacturing (AM), and for improved performance and scalability. Here, an extension to the time–temperature history input data format and the core ExaCA algorithm to include an arbitrary number of melting and solidification events yielded improved prediction of texture for various melt pool geometries, expanding the range of AM-relevant conditions that can be accurately simulated. Improved heat transport process simulation coupling, including the creation of large raster datasets from single track time–temperature history data and in-memory coupling with the new, performance portable finite difference code Finch, were also demonstrated in example studies on the effect of multilayer AM microstructure predictions on hatch spacing and cell size, respectively. Additional new features are detailed and demonstrated, including the ability to perform simulations using various interfacial response function forms, execute simulations on state-of-the-art hardware, improved usability through post-processing versatility, and improved strong and weak scaling performance. The performance, physics, and versatility improvements demonstrated here will further enable large-scale studies on AM process–microstructure relationships that were not previously possible. Furthermore, the usability improvements and ability to run coupled AM process–microstructure simulations using the Finch-ExaCA workflow will facilitate broader use of this open-source software by the computational materials community.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Using AI to Reproduce Neutrino Cross Section Analysis - Prototyping the Neutrino Discovery Platform

The Neutrino Discovery Platform (NDP) aims to accelerate DUNE-era science by making the neutrino program's existing datasets analyzable through fast, reproducible, and auditable workflows. We report a working version of two of its layers, data curation and agentic orchestration, built and tested end to end on MINERvA open data. The guiding lesson throughout is that a cross section is a measurement, and not just a plotted shape, only if it carries a defensible systematic-uncertainty budget, a trustworthy unfolding, and a reproducible record. Using a single medium-energy playlist pair from the MINERvA open-data release (about $2.05\times10^{17}$ protons on target of data), we first reproduced the shapes of two published charged-current inclusive $\nu_\mu$ measurements through a complete extraction ladder: selection, background subtraction, D'Agostini unfolding, efficiency correction, and flux normalization. These shape-level reproductions ran and tracked the published results, but they lacked the systematic-uncertainty machinery that defines a MINERvA cross section. To supply it, we vendored and built the MINERvA Analysis Toolkit and developed a many-universe systematic-uncertainty tool that produces a portable covariance artifact, a parallel event-loop runner, and a per-run auditability harness. Validated against a published covariance release, the toolchain reproduces the released statistical, flux, and muon-energy-scale terms and shows that they account for roughly 63\% of the total variance, with the remainder unreleased. Using this same infrastructure, we then performed a measurement of our own design, the hadronic recoil-energy distribution of low-energy ($E_\nu<2.5$~GeV) charged-current inclusive events, and found data/simulation shape agreement of $\chi^2/\mathrm{ndf}=1.26$. Together these results show that the platform supports original physics and not only reproductions.

Breaux, Auto [Tulane U. (main)]↗

DOE OSTI2026

Toward the Neutrino Discovery Platform: An Auditable, Uncertainty-Bearing Toolchain for MINERvA Open-Data Cross-Section Analysis

Breaux, Auto [Tulane U. (main)]↗