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DOE OSTI2026

Brochure for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

In February of 2025 a joint ASCR/BER workshop was held to identify key transformational research directions for understanding biology using artificial intelligence (AI), digital twins and high-performance (HPC) computational methods to facilitate scientific discovery and innovation in support of the Department of Energy mission. AI technologies offer exciting new groundbreaking methods to analyze large volumes of complex biological data, thereby greatly accelerating the ability to understand, predict, and design biological processes for beneficial purposes. In the laboratory, the bridging of AI-enabled automated experimental technologies, HPC and digital twins will provide potent tools for researchers to explore the fundamental nature of biology and harness its inherent metabolic potential for a variety of beneficial purposes. The focus of this workshop was on how high-performance computational methods can impact this objective by exploring digital twins, foundational models, and data-driven approaches with applications to advance automated laboratory experiments, modeling of complex living systems and engineering new functions into plants and microbial systems relevant to DOE mission. Workshop attendees with expertise in plant science, microbiology, mathematics, computer science, and AI assessed the current state of the science, trends, and AI challenges at the interface of plant and microbial systems biology and computational science to identify opportunities for high-impact research. This collaborative effort capitalized on ASCR's advancements in applied mathematics, computer science, and Exascale systems, and BER's expertise in basic genomics-enabled research on DOE relevant plant and microbial systems. The workshop culminated in four key priority research directions to guide future research and development within DOE Office of Science programs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Misclassification of causes of death among a small all-autopsied group of former nuclear workers: Death certificates vs. autopsy reports

The U.S. Transuranium and Uranium Registries performs autopsies on each of its deceased Registrants as a part of its mission to follow up occupationally-exposed individuals. This provides a unique opportunity to explore death certificate misclassification errors, and the factors that influence them, among this small population of former nuclear workers. Underlying causes of death from death certificates and autopsy reports were coded using the 10 th revision of the International Classification of Diseases (ICD-10). These codes were then used to quantify misclassification rates among 268 individuals for whom both full autopsy reports and death certificates with legible underlying causes of death were available. When underlying causes of death were compared between death certificates and autopsy reports, death certificates correctly identified the underlying cause of death’s ICD-10 disease chapter in 74.6% of cases. The remaining 25.4% of misclassified cases resulted in over-classification rates that ranged from 1.2% for external causes of mortality to 12.2% for circulatory disease, and under-classification rates that ranged from 7.7% for external causes of mortality to 47.4% for respiratory disease. Neoplasms had generally lower misclassification rates with 4.3% over-classification and 13.3% under-classification. A logistic regression revealed that the odds of a match were 2.8 times higher when clinical history was mentioned on the autopsy report than when it was not. Similarly, the odds of a match were 3.4 times higher when death certificates were completed using autopsy findings than when autopsy findings were not used. This analysis excluded cases where it could not be determined if autopsy findings were used to complete death certificates. The findings of this study are useful to investigate the impact of death certificate misclassification errors on radiation risk estimates and, therefore, improve the reliability of epidemiological studies.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2020

National Reactor Innovation Center NRC Early Site Permit Roadmap

The National Reactor Innovation Center (NRIC) at Idaho National Laboratory (INL) in Idaho Falls, Idaho, was authorized under the Nuclear Energy Innovation Capabilities Act (Public Law 115–248) to provide private sector technology developers with resources and infrastructure for testing, demonstration, and performance assessment to accelerate deployment of new advanced reactor technology concepts. NRIC’s mission is to enable clean, affordable, reliable energy by supporting U.S. government investments in nuclear energy research, development, demonstration, and commercialization of new nuclear energy systems. It intends to provide existing facilities and other undeveloped and previously developed sites at INL to advanced reactor vendors for prototype technology testing and deployment. Some of these demonstration projects may be subject to DOE authorization, while other projects may require approval by the U.S. Nuclear Regulatory Commission (NRC) prior to construction and operation. NRIC and INL are considering whether to pursue one or more ESPs to help facilitate new reactor deployment at the INL Site. An ESP would allow early investment in licensing infrastructure in advance of a reactor design being ready to deploy. Through the ESPs, NRIC would help reduce the cost and time required to deploy demonstration reactors at the INL Site. This also will allow INL to bring its nuclear and siting experience to the ESP development process allowing for better and more efficient improvements and utilization of the Site. A preapproved ESP at INL will also remove the uncertainty of NRC siting approvals from the demonstration project proponents, who may not be well positioned to complete the analyses in a timely manner. The purpose of this NRIC ESP roadmap is to provide rigor and understanding needed for INL and DOE decision-makers to make data-informed decisions on pursuing NRC ESPs in support of future advanced reactor demonstrations at INL. The roadmap provides a structured method for addressing potential challenges and identifying options for resolution.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

National Reactor Innovation Center NRC Early Site Permit Roadmap

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Next Generation Solvent Vapor Pressure Testing

Previous work by Savannah River National Laboratory (SRNL) indicated that the actual Next Generation Solvent vapor pressure would be higher than the Original Caustic Side Solvent Extraction (CSSX) solvent vapor pressure, but below a bounding vapor pressure using Raoult’s Law. Since solvent vapor pressure has a significant impact on Composite Lower Flammability Limits (CLFL) and attendant accident analyses, obtaining an additional margin from the bounding NGS vapor pressure would be valuable. In order to quantify how much margin might be gained from the NGS bounding solvent, SRNL researchers were requested to perform vapor pressure testing with the Next Generation Solvent (NGS) by Savannah River Mission Completion (SRMC). The vapor pressure curve for the NGS formulation set to be deployed at the Salt Waste Processing Facility (SWPF) has been determined by SRNL up to 55°C (131°F) using headspace Gas Chromatography (GC). It was expected that NGS would have a higher vapor pressure than the Original CSSX solvent; however, experimental results indicate a lower vapor pressure. At this time, it is uncertain if this difference is due to the 7x increase in concentration of the large calixarene in the solvent (0.007M BOBCalix in Original CSSX solvent vs. 0.05M MaxCalix in NGS) or to minor batch-to-batch variations in Isopar-L constituents. The NGS and the Original CSSX solvent vapor pressure data were fitted to the Antoine Equation. The Antoine equation gives a more accurate representation of the expected vapor pressure of the solvents outside of the temperature range tested. The Antoine equation fitting for NGS is given below: $P=10^{6.364⁻\frac{1788.4}{T+219.3}}$. Where, p is the vapor pressure (partial pressure) of NGS in mmHg and T is the temperature in °C. It is recommended that SRMC either continue using the more conservative Isopar-L vapor pressure calculations at SWPF with the equation developed for the Original CSSX solvent, or the equation presented above for the NGS solvent. Additionally, it is recommended to study the variability in Isopar-L vapor pressure between lots.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

ARENA: Adversary-Resistant Evolving Neural Architectures

Neural networks are becoming the cornerstone for national security prediction tasks. However, designing them requires significant research and trial/error, as they have many hyperparameters, including their computation graph (“architecture”). Neural architecture search (NAS) employs secondary optimizers to search for architectures maximizing objectives like accuracy. Evolutionary algorithms (EAs) are the most used class of optimizer for NAS. However, existing Python libraries for writing EAs limit the complexity of experiments a user can design. In this project, we built ARENA, a Python framework that encodes complex, hyper-realistic EAs. ARENA collects detailed information as it runs and is flexible enough to encode non-EA search algorithms. We tested ARENA on 4 toy optimization problems by encoding 3 search algorithms for each—random search, an EA, and simulated annealing. We also designed an EA that performs NAS on the MNIST dataset. Our experiments suggest the potential for immediate mission impact through solving lab-wide optimization problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Tank 14H Preliminary Cease Waste Removal Sample Analyses

Savannah River Mission Completion Waste Removal and Tank Closure requested that Savannah River National Laboratory perform characterization analyses of Tank 14H in support of closure activities, specifically preliminary cease waste removal. This report provides important characterization of the prepared filtrate and air-dried solids in the January 2026 Tank 14H sample, HTF-14-26-1,2,3,4,5,6, for I-129, Tc-99, Cs-137, and Sr-90. The results reported here were utilized by SRMC to declare preliminary cease waste removal for Tank 14H.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Constraining Mornings and Evenings on Distant Worlds: A new Semianalytical Approach and Prospects with Transmission Spectroscopy

The technique of transmission spectroscopy—the variation of a planetary radius with wavelength due to opacity sources in the planet’s terminator region—has been one of the most successful in the characterization of exoplanet atmospheres to date, providing key insights into the composition and structure of these distant worlds. A common assumption made when using this technique, however, is that the variations are the same in the entire terminator region. In reality, the morning and evening terminators might have distinct temperature, pressure, and thus compositional profiles due to the inherent 3D nature of the planet, which would, in turn, give rise to different spectra on each side of it. Constraining those might be fundamental for our understanding of not only the weather patterns in these distant worlds but also the planetary formation signatures that might only be possible to extract once these features are well understood. Motivated by this physical picture, in this work, we perform a detailed study of the observational prospects of detecting this effect. We present an open-source semianalytical framework with which this information can be extracted directly from transit light curves and perform a detailed study of the prospects of detecting the effect with current missions, such as TESS, and upcoming ones, such as JWST. Our results show that these missions show great promise for the detection of this effect. Transmission spectroscopy studies with JWST, in particular, could provide spectra of each of the limbs, allowing us to convey 3D information previously accessible only via phase curves.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

NSE workplace of the future: Enabling a LPS ready workforce

The future mission success of the Nuclear Security Enterprise (NSE) relies on our workforce and our workplace. The 2022 Nuclear Posture Review notes that “the health of the enterprise depends critically on recruiting and retaining a skilled and diverse workforce” and the 2022 National Nuclear Security Administration (NNSA) Strategic Vision articulates a commitment to “recruit, invest in, and nourish a high-performing, diverse, and flexible workforce that can meet the unique policy, technical, and leadership needs of our mission today and well into the future.”

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Performance Limits for Airborne Weather Detection Radar

An aircraft commander needs to be aware of weather phenomena that might be hazardous to his aircraft and mission. An important tool for this is airborne weather (WX) detection radar. The airborne WX radar needs to map weather for the aircraft commander that might be relevant to the safety of the aircraft, which involves both detecting a weather phenomenon, and to some extent seeing through it to detect weather phenomena behind it. Many factors influence the performance of an airborne WX radar

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

PreMevE Update: Forecasting Ultra-Relativistic Electrons Inside Earth's Outer Radiation Belt

Energetic electrons inside Earth's Van Allen belts pose a major radiation threat to spaceborne electronics that often play vital roles in modern society. Ultra-relativistic electrons with energies greater than or equal to two megaelectron-volt (MeV) are of particular interest, and thus forecasting these ≥2 MeV electrons has a significant meaning to all space sectors. Here, we update the latest development of the predictive model for MeV electrons in the outer radiation belt. The new version, called PREdictive MEV Electron (PreMevE)-2E, forecasts ultra-relativistic electron flux distributions across the outer belt, with no need for in situ measurements of the trapped MeV electron population except at the geosynchronous orbit (GEO). Model inputs include precipitating electrons observed in low-Earthorbits by NOAA satellites, upstream solar wind speeds and densities from solar wind monitors, as well as ultra-relativistic electrons measured by one Los Alamos GEO satellite. We evaluated 32 supervised machine learning models that fall into four different classes of linear and neural network architectures, and successfully tested ensemble forecasting by using groups of top-performing models. All models are individually trained, validated, and tested by in situ electron data from NASA's Van Allen Probes mission. It is shown that the final ensemble model outperforms individual models at most L-shells, and this PreMevE-2E model can provide 25-h (~1-day) and 50-h (~2-day) forecasts with high mean performance efficiency and correlation values. Our results also suggest that this new model is dominated by nonlinear components at L-shells <~4 for ultra-relativistic electrons, different from the dominance of linear components for 1 MeV electrons as previously discovered.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Predictions of core plasma performance for the SPARC tokamak

SPARC is designed to be a high-field, medium-size tokamak aimed at achieving net energy gain with Ion Cyclotron Range-of-Frequencies (ICRF) as its primary auxiliary heating mechanism. Empirical predictions with conservative physics indicate that SPARC baseline plasmas would reach Q~11, well above its mission objective of Q>2. To build confidence that SPARC will be successful, physics-based integrated modeling has also been performed. The TRANSP code coupled with the theory-based TGLF turbulence model and EPED predictions for pedestal stability find that Q~9 is attainable in standard H-mode operation and confirms Q>2 operation is feasible even with adverse assumptions. In this analysis, ion cyclotron waves are simulated with the full wave TORIC code and alpha heating is modeled with the Monte-Carlo fast ion NUBEAM module. Detailed analysis of expected turbulence regimes with linear and nonlinear CGYRO simulations is also presented, demonstrating that profile predictions with the TGLF reduced model are in reasonable agreement.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Semi-supervised Bayesian Low-shot Learning

Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Water Power Technologies Office: Multi-Year Program Plan

The U.S. Department of Energy’s Water Power Technologies Office (WPTO) has released its first Multi-Year Program Plan (MYPP), which outlines the Office’s research priorities and plans through 2025. Cover of the WPTO MYPP 2022. The MYPP serves as both a strategic vision and an operational guide to help WPTO manage and coordinate its activities, as well as a vehicle to communicate WPTO’s mission, goals, and plans to water power stakeholders and the public. It details WPTO’s research, development, demonstration, and commercial activities across both hydropower and marine energy for the coming years and outlines how these efforts can help meet the nation’s energy and sustainability goals. The MYPP features key performance goals for 2025, as well as additional objectives running through 2030. All outlined goals contribute to the overarching mission of WPTO to enable research, development, and testing of new technologies to advance marine energy and hydropower systems for a flexible, reliable grid.

13 HYDRO ENERGY↗

DOE OSTI2020

Advances in GDSA Framework Development and Process Model Integration

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and highlevel nuclear waste (HLW). A high priority for SFWST disposal R&D is to develop a disposal system modeling and analysis capability for evaluating disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2020 advances of the Geologic Disposal Safety Assessment (GDSA) Framework and PFLOTRAN development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to probabilistically assess the performance of disposal options and generic sites. The capability is a framework called GDSA Framework that employs high-performance computing (HPC) capable codes PFLOTRAN and Dakota.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Hanford Site Revegetation Monitoring Report for Fiscal Year 2020

This report describes the monitoring of areas revegetated by the River Corridor Closure Contractor (RCCC) and CH2M Hill Plateau Remediation Company (CHPRC) that were transitioned to and monitored by Mission Support Alliance (MSA) in 2020, along with sites revegetated by MSA. Site monitoring is a continuance of efforts performed by the RCCC from fiscal year (FY) 2007 through FY 2020. This report contains data collected in 2020 documenting the recovery of revegetation areas associated with the Comprehensive Environmental Response, Compensation, and Liability Act of 1980 cleanup of National Priorities List waste sites and restoration of lands disturbed by ongoing Site mission activities at the Hanford Site in Richland, Washington. It contains vegetation monitoring data for 41 sites selected to be representative sites for areas planted between the years of FY 2007 and FY 2020.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

QRF4P-NRT: Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates Using Quantile Regression Forests

Accurate and reliable near-real-time satellite precipitation estimation is of great importance for operational large-scale flood forecasting and drought monitoring. The state-of-the-art precipitation post-processing model is based on a deterministic approach to construct relationships between satellites estimates and ground observations. We propose a probabilistic postprocessor, the Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates using Quantile Regression Forests (QRF4P-NRT), based on quantile modeling, yielding both deterministic and probabilistic predictions. The experimental design incorporates different solutions of near-real-time predictors to further improve the model performance. Using the Integrated Multi-satellitE Retrievals Early Run for Global Precipitation Measurement Mission (IMERG-E) product as an example, we illustrate that the proposed method significantly improves the overall quality of the raw IMERG-E and is also superior to the bias-corrected product (IMERG Final Run, IMERG-F) at daily scale in a complex mountain basin. Evaluations of the corrected IMERG-E, raw IMERG-E, and IMERG-F using ground observation show that the corrected IMERG-E improves correlation coefficients (0.7), mean error (-0.14 mm/day) and root mean square error (3.3 mm/day) relative to the raw IMERG-E (0.31, -0.72 and 5.5 mm/day) and IMERG-F (0.34, -0.09 and 6.0 mm/day). The error decomposition further confirms that the QRF4P-NRT improves on the various deficiencies of the raw IMERG-E product. The ensemble assessment also demonstrates that the quantile outputs provide reliable prediction spread and sharp prediction intervals. The promising results indicate the great potential of the proposed method for probabilistic post-processing for near-real-time satellite precipitation estimates, and for further applications such as hydrological ensemble forecasting.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The eROSITA Final Equatorial-Depth Survey (eFEDS)

Theoretical models of the co-evolution of galaxies and active galactic nuclei (AGNs) ascribe an important role in the feedback process to a short, luminous, obscured, and dust-enshrouded phase during which the accretion rate of the supermassive black hole is expected to be at its maximum and the associated AGN-driven winds are also predicted to be maximally developed. Here, to test this scenario, we have isolated a textbook candidate from the eROSITA Final Equatorial-Depth Survey (eFEDS) obtained within the performance and verification program of the eROSITA telescope on board the Spectrum Röntgen Gamma mission. From an initial catalogue of 246 hard X-ray selected sources that are matched with the photometric and spectroscopic information available within the eROSITA and Hyper Suprime-Cam consortia, three candidates quasars in the feedback phase have been isolated applying a diagnostic proposed previously. Only one source (eFEDS J091157.4+014327) has a spectrum already available (from SDSS-DR16, z = 0.603) and it unambiguously shows abroad component (full width at half maximum ~1650 kms –1 ) in the [OIII]5007 line. The associated observed L [OIII] is ~2.6 × 10 42 erg s –1 , one to two orders of magnitude higher than that observed in local Seyfert galaxies and comparable to those observed in a sample of z ~ 0.5 type 1 quasars. From the multi-wavelength data available, we derive an Eddington ratio (L bol /L Edd ) of ~0.25 and a bolometric correction in the hard X-ray band of k bol ~ 10, which is lower than the corrections observed for objects at similar bolometric luminosity. These properties, along with the outflow, the high X-ray luminosity, the moderate X-ray obscuration (L X ~10 44.8 erg s –1 , N H ~2.7 × 10 22 cm –2 ), and the red optical colour, all match the prediction of quasars in the feedback phase from merger-driven models. Forecasting to the full eROSITA all-sky survey with its spectroscopic follow-up, we predict that by the end of 2024, we will have a sample of few hundred such objects at z= 0.5–2.

79 ASTRONOMY AND ASTROPHYSICS↗