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DOE OSTI2022

Linking climate change and human systems: a case study of Arctic pipelines

This case study estimates the potential economic risk from permafrost thaw on oil and gas pipelines in the Russian Arctic as part of a larger effort to better understand complex interactions between human and earth systems in the Arctic. Pan-Arctic simulations of permafrost thaw-depth from the Community Land Model version 4.5 and ground ice characteristics were used to generate ground subsidence projections over the period 2020 to 2040 with a quantification of uncertainty. Russian oil and gas transmission pipeline networks were then overlaid on the permafrost thaw projections in ArcGIS to identify pipelines vulnerable to damage from permafrost thaw. Recent pipeline construction costs were used to estimate the total replacement costs for at-risk pipelines under several thaw scenarios. The results indicate that permafrost thaw poses a major threat to pipeline infrastructure, especially gas pipelines, in the Russian Arctic. Over the twenty-year study period, total replacement costs for oil and gas pipelines were estimated at $\$$110 billion in 2020 USD. Reduced economic viability of pipelines under climate change will likely trigger major shifts in the Russian oil and gas industry, which would have impacts on markets, emissions, and geopolitics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Entry and Disposition of Zika Virus Immune Complexes in a Tissue Culture Model of the Maternal-Fetal Interface

Zika virus (ZIKV) infections have been associated with an increased incidence of severe microcephaly and other neurodevelopmental disorders in newborn babies. Passive immunization with anti-ZIKV neutralizing antibodies has the potential to become a feasible treatment or prophylaxis option during pregnancy. Prior to clinical use, such antibodies should be assessed for their ability to block ZIKV passage to the fetus. We used human placental and mammalian cell monolayers that express FcRn and laboratory preparations of anti-ZIKV antibodies as a model system to investigate the disposition of ZIKV/antibody immune complexes (ICs) at the maternal-fetal interface. We further characterized solution properties of the ICs to evaluate whether these are related to in vitro effects. We found that both ZIKV and ZIKV envelope glycoprotein can enter and passage through epithelial cells, especially those that overexpress FcRn. In the presence of ZIKV antibodies, Zika virus entry was bimodal, with reduced entry at the lowest (0.3–3 ng/mL) and highest (µg/mL) antibody concentrations. Intermediate concentrations attenuated inhibition or enhanced viral entry. With respect to anti-ZIKV antibodies, we found that their degradation was accelerated when presented as ICs containing increased amounts of ZIKV immunogen. Of the two monoclonal antibodies tested, the preparation with higher aggregation also exhibited higher degradation. Our studies confirm that intact Zika virus and its envelope immunogen have the potential to enter and be transferred across placental and other epithelial cells that express FcRn. Presence of anti-ZIKV IgG antibodies can either block or enhance cellular entry, with the antibody concentration playing a complex role in this process. Physicochemical properties of IgG antibodies can influence their degradation in vitro.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Ecosystems for Scientific Computing in the Age of AI

Scientific computing is at an inflection point. Artificial intelligence (AI) is reshaping how scientific software is developed, how teams collaborate, how projects are governed, and how the next generation is trained. Drawing on insights from a 2025 workshop report, this article argues that the future of discovery will depend on agile, robust ecosystems built through socio-technical co-design—the intentional integration of technical and human systems. This perspective is essential for ensuring that future scientific computing remains trustworthy, sustainable, and scalable. It combines advances in AI, high-performance computing, and software with new models for cross-disciplinary collaboration, education, and workforce development. Key recommendations include building modular, trustworthy AI-enabled software ecosystems; enabling teams to integrate AI into scientific workflows while preserving human creativity, integrity, and rigor; and developing adaptive training pathways that keep pace with rapid technological change. By sharing these perspectives, we hope to stimulate broader community dialogue and encourage coordinated action.

AI↗

DOE OSTI2026

Generative AI for Grid Operations [Slides]

In the last few years, the development and use of generative artificial intelligence (AI) and large-language models (LLMs) have changed the landscape of how AI and machine learning (ML) are being used in power systems. LLMs are built on foundational models based on large data sets that can be trained to provide information rapidly and through simple natural language prompts. Generative AI can then perform human-like tasks using ML models to identify and mimic pattens in the data sets. This presentation explores how generative AI can enhance grid operations by improving forecasts, enabling rapid contingency analyses, and offering real-time operational suggestions. By providing grid operators with valuable insights, generative AI will empower them to manage power systems more effectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Enhanced Oblique Decision Tree Enabled Policy Extraction for Deep Reinforcement Learning in Power System Emergency Control

Deep reinforcement learning (DRL) algorithms have successfully solved many challenging problems in various power system control scenarios. However, their decision-making process is usually regarded as black-boxes. Furthermore, how DRL models interact with human intelligence remains an open problem. Thus, this paper proposes a policy extraction framework to extract a complex DRL model into an explainable policy. This framework includes three parts: 1) DRL training and data generation. We train an agent for a specific control task and generate data, which contains the control policy of the agent. 2) Policy extraction. We propose an information gain rate based weighted oblique decision tree (IGR-WODT) for DRL policy extraction. 3) Policy evaluation. We define three metrics to evaluate the performance of the proposed approach. A case study for the under-voltage load shedding problem shows that the IGR-WODT presents a performance enhancement compared with DRL, weighted oblique decision tree, and univariate decision tree. The proposed policy extraction method could provide an intuitive explanation of the neural network decision-making process to the dispatchers when making final decisions on power grid operation. Also, the resulted rule-based controller could replace the deep neural network-based controller in many field edge devices with limited computing resources, providing comparable performance.

deep reinforcement learning↗

DOE OSTI2020

Development of a Conditioned System-Level Groundwater Model to Evaluate Long-Term Groundwater Impacts within a Performance Assessment - 20115

A preliminary performance assessment (PA) of single-shell tank Waste Management Area (WMA) A-AX located at the U.S. Department of Energy (DOE)'s Hanford Site in southeastern Washington is being conducted to satisfy the requirements of DOE Order 435.1 [1] as it relates to closure of the single-shell radioactive waste tanks in the WMA A-AX tank farms. A PA assesses the fate, transport, and impacts of radionuclides within a low-level radioactive waste disposal facility in its assumed closure configuration and the subsequent potential doses to humans over a 1,000-year compliance period and a 10,000-year performance evaluation period. The WMA A-AX preliminary PA evaluation is structured around the complementary use of process-level and system-level models to calculate the facility performance against established DOE Order 435.1 [1] performance objectives. Process-level models are those that represent a detailed phenomenological representation of processes of concern in the PA. Process models typically only represent one or a few of the components of the PA, such as groundwater flow and transport, and must be integrated with other modeling elements to perform PA calculations. System-level models are those that are abstracted from the process models, retaining the essential features of the process model, while allowing integration of all aspects of the PA in a single modeling framework. System-level models are often characterized by coarser numerical discretization, lower dimensionality, or other similar simplifications compared to the process-level model. Traditionally, a three-dimensional (3-D) process-level model is utilized primarily to evaluate the long-term impact on groundwater and the potential doses to individuals who consume contaminated groundwater. System-level models are also utilized to evaluate the groundwater pathway in PAs. These models typically have reduced dimensionality (1-D) and are conditioned utilizing flow fields (Darcy fluxes) and moisture content distributions that are abstracted from the process level models. The abstraction approach assures that the flow field in both models is consistent for a specific set of input parameters for flow, differing only in the discretization and dimensionality of the two models. The preliminary WMA A-AX PA incorporates a detailed representation of the geological system and hydraulic properties within the 3-D model STOMP{sup C} numerical code so that the effects of relevant features and processes on water flow and radionuclide transport in the subsurface can be evaluated. The complementary system-level model is developed utilizing the GoldSim{sup C} code to implement a simplified, 1-D equivalent model to represent the groundwater pathway. Contaminant transport through the vadose zone and unconfined aquifer for Tc-99 and I-129 were evaluated in each of the models. Adjustments to the saturated portion of the GoldSim{sup C} 1-D model were required to mimic the dispersion effect captured with the 3-D STOMP{sup C} model. Once this conditioning was conducted, highly similar results for the transport of Tc-99 and I-129 were achieved at the point of calculation, located 100 m downgradient from the WMA A-AX fenceline. These radionuclides represent elements that are regarded as primary dose drivers in the PA groundwater pathway analysis. The high degree of conformance between the two models suggests that the use of the equivalent 1-D system model is suitable for evaluating the full suite of radionuclides that will be released from the tank sources within WMA A-AX over the 1,000-year compliance period and over the 10,000-year evaluation period. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation

Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.

AI-driven discovery↗

DOE OSTI2022

HI-STORM Overpack and MPC-32 Thermal-Hydraulic Model with MOOSE Framework

Nuclear power is a significant source of electricity in the United States, but the average age of nuclear power plants is around 40 years old. The safe management of the spent nuclear fuel (SNF) is a key aspect of the back-end of the nuclear fuel cycle. Spent fuel dry storage systems are becoming a popular and effective solution in this area, given the absence of a final disposal system. The spent fuel cask system (dry cask method) provides a feasible solution to maintain spent fuel for 60 years before final disposal. Dry cask storage has many characteristics that make it attractive. It fulfills the safety requirements of the Nuclear Regulatory Commission (NRC) while providing modularity and flexibility to contractors. The HI-STORM overpack and MPC-32 canister are the main parts of the HI-STORM 100 dry cask storage system. These components remove heat from the system using natural circulation, requiring no human intervention. This is the characteristic that provides passive heat removal and low maintenance features in dry cask storage systems. To develop a thermal model for a dry cask storage system, the physics behind the system should be defined clearly. There are two natural circulation loops in the system; circulation of helium cools down the nuclear assemblies in the MPC, while circulation of air cools down the walls of the MPC. This work aims to develop a thermal model of the MPC-32 canister and HI-STORM overpack using the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE is an open-source framework developed by Idaho National Laboratory (INL) for multiscale, multiphysics simulations. In this study, we will investigate and demonstrate the thermal-hydraulics modeling capabilities of the MOOSE framework, including natural circulation, heat transfer, and porous flow.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Development of a MOOSE thermal model of the MPC-32 canister and HI-STORM overpack

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Integrating field observations and process-based modeling to predict watershed water quality under environmental perturbations

Watersheds play a critical role in supplying water resources needed for human use and ecosystem health. Understanding and predicting how, when, and where changes in the quantity and quality of water resources occur under different environmental stresses including extreme events is crucial for sustainable management of water resources under a changing environment. However, few studies have attempted to quantify or identify the factors and process interactions controlling the impact of extreme events across water-shed systems. Only few large-scale studies include coordinated monitoring and modeling efforts, which limits our ability to assess the large-scale impact of extreme events on water supply and quality. Methods are lacking to propagate uncertainty in process understanding through an integrated hydro-biogeochemical model framework and evaluate its importance, thus failing to take full advantage of the information potentially available through transformative advances in characterization technologies from high-resolution mass spectrometry to airborne and satellite-based remote sensing. There are consequent risks to our nations water security and to human and ecosystem health that may become exacerbated with the increasing frequency of extreme events that is projected for the coming decades. This paper reviews the current status of watershed science for both water quantity and quality and identifies critical gaps in our current knowledge and modeling capability in addressing the emergent needs in predicting watershed hydrologic and biogeochemical responses (i.e., water quantity and quality) under natural and anthropogenic perturbations. We highlight the need to (1) understand how environmental perturbations including extreme events like floods and droughts propagate through watershed systems and assess their short- and long-term impacts on watershed biogeochemistry, water quality and their recovery pathways; (2) develop and improve a watershed water quality model that reflects the state of scientific understanding gained from observations; and (3) construct a data-model fusion system for watershed characterization, process identification, and mechanistic model parameterization. A large base of modeling, monitoring and data capabilities have been built by various federal government agencies given the relevance of water to their critical missions. An emerging need is to build an integrated national capability for watershed water availability and quality that can address water-related missions across multiple federal agencies.

13 HYDRO ENERGY↗

DOE OSTI2022

Space weather observations, modeling, and alerts in support of human exploration of Mars

Space weather observations and modeling at Mars have begun but they must be significantly increased to support the future of Human Exploration on the Red Planet. A comprehensive space weather understanding of a planet without a global magnetosphere and a thin atmosphere is very different from our situation at Earth so there is substantial fundamental research remaining. It is expected that the development of suitable models will lead to a comprehensive operational Mars space weather alert (MSWA) system that would provide rapid dissemination of information to Earth controllers, astronauts in transit, and those in the exploration zone (EZ) on the surface by producing alerts that are delivered rapidly and are actionable. To illustrate the importance of such a system, we use a magnetohydrodynamic code to model an extreme Carrington-type coronal mass ejection (CME) event at Mars. The results show a significant induced surface field of nearly 3,000 nT on the dayside that could radically affect unprotected electrical systems that would dramatically impact human survival on Mars. Other associated problems include coronal mass ejection (CME) shock-driven acceleration of solar energetic particles producing large doses of ionizing radiation at the Martian surface. In summary, along with working more closely with international partners, the next Heliophysics Decadal Survey must include a new initiative to meet expected demands for space weather forecasting in support of humans living and working on the surface of Mars. It will require significant effort to coordinate NASA and the international community contributions.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Sediment transport and morphodynamic changes due to upstream variation and sea level rise on the Western Mekong Delta, Vietnam

The hydrological and morphological dynamics across the Vietnam Mekong Delta (VMD) coastline are increasingly shaped by human interventions and climate change, affecting the sustainability of socio-economic environments. This study employs the TELEMAC-MASCARET modeling system (TELEMAC-2D, TOMAWAC, and SISYPHE modules) to simulate hydrodynamics, sediment transport, and morphological changes for a 10-year period under baseline (SC1) and future climate (SC2) scenarios, considering wave climatology of Northeast (NEM) and Southwest monsoons (SWM). The results of sedimentation and erosion were analyzed across five key coastal zones from Ca Mau Cape (KV1) to Ha Tien (KV5). Model results highlight distinct seasonal patterns of sediment redistribution shaped by monsoonal flows, wave action, and seabed morphology. During the NEM, sediment deposition is concentrated near estuarine zones, while the SWM favors offshore sediment accumulation due to perpendicular wave actions, and shallow, gently sloping bathymetry that dissipates wave energy. Coastal erosion predominantly occurs at convex shoreline features subjected to stronger coastal currents, particularly in fine-grained sediment areas. Under future sea level rise and land subsidence scenarios, reduced flow velocities lead to lower bottom shear stress, promoting deposition and mitigating erosion problems despite increased wave energy. Besides that, sediment transport analyses across 14 cross-sections (C-S) reveal a southward trend during the NEM and a northward reversal during the SWM, with upstream sediment reductions primarily influencing near-estuarine zones and exerting minimal impact beyond the mid-coastal regions. Furthermore, these findings advance the understanding of sediment redistribution processes under climate change and seasonal change, and provide a scientific basis for adaptive coastal management and sustainable development.

Climate change↗

DOE OSTI2023

Global monthly sectoral water use for 2010–2100 at 0.5° resolution across alternative futures

Water usage is closely linked with societal goals that are both local and global in scale, such as sustainable development and economic growth. It is therefore of value, particularly for long-term planning, to understand how future sectoral water usage could evolve on a global scale at fine resolution. Additionally, future water usage could be strongly shaped by global forces, such as socioeconomic and climate change, and the multi-sector dynamic interactions those forces create. We generate a novel global gridded monthly sectoral water withdrawal and consumption dataset at 0.5° resolution for 2010–2100 for a diverse range of 75 scenarios. The scenarios are harmonized with the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios to support its usage in studies evaluating the implications of uncertain human and earth system change for future global and regional dynamics. To generate the data, we couple the Global Change Analysis Model (GCAM) with a land use spatial downscaling model (Demeter), a global hydrologic framework (Xanthos), and a water withdrawal downscaling model (Tethys).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Application of Artificial Intelligence/Machine Learning to Operations Research

This report examines the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on operations research, private industry, and government sectors, highlighting their applications in automating processes, enhancing decision-making, and optimizing complex systems. AI/ML technologies have revolutionized industries through predictive maintenance, supply chain optimization, and autonomous systems, while also advancing public safety and defense operations. However, challenges such as data integrity, model transparency, and the need for human oversight persist, particularly in high-consequence environments. The report emphasizes the critical role of explainable AI (XAI) and human-computer interaction models like Human-in-the-Loop (HITL) and Human-on-the-Loop (HOTL) in fostering trust and accountability. Balancing automation with ethical responsibility and transparency is essential for the continued successful integration of AI/ML into operational and strategic decision-making frameworks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Implementation and Evaluation of Emission‐Driven Land‐Atmosphere Coupled Simulation in E3SMv2.1

Emissions-driven (prognostic CO 2 ) simulations are essential for representing two-way carbon-climate feedback in Earth System Models. We present an emissions-driven land–atmosphere coupled biogeochemistry (BGC) configuration (BGCLNDATM_progCO2) in version 2.1 of the Energy Exascale Earth System Model (E3SMv2.1). This is the first E3SM configuration that performs land-atmosphere emission-hindcasts. Here, we document its implementation, evaluate the model's performance against observations and other models, and propose a structured evaluation protocol for such emissions-driven simulations. We conducted transient historical simulations (1850–2014) with BGCLNDATM_progCO2 and compare them to reference simulations—a land-atmosphere coupled simulation without BGC and a standalone land simulation with BGC, both using prescribed CO 2 concentrations—and to observations. BGCLNDATM_progCO2 overestimates atmospheric CO 2 concentrations by 11–23 ppm yet stays within the 40-ppm spread CMIP6 emission-driven models and retains physical climate properties comparable to the reference runs. The CO 2 biases are partly attributed to underrepresented oceanic CO 2 uptake and inadequate representations of some terrestrial processes. In general, introducing prognostic CO 2 did not change physical climate metrics at the global scale but had larger regional effects, particularly over land where spatially heterogeneous CO 2 and prognostic leaf area index influenced surface energy balance. Finally, we propose a general evaluation protocol including spin-up assessment, atmospheric CO 2 benchmarking, physical climate evaluation, and land biogeochemical analysis to support scientific rigor and facilitate inter-model comparisons. The new configuration lays the groundwork for future enhancements, including improved terrestrial biogeochemical processes, integrated marine biogeochemistry, and additional human–Earth system interactions. These developments advance E3SM toward fully coupled emissions-driven simulations, enabling more accurate carbon–climate feedback projections and informing mitigation policy by providing physically consistent carbon-budget metrics for mitigation scenarios.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗

DOE OSTI2022

Predicting transport of intra-articularly injected growth factor fusion proteins into human knee joint cartilage

There are no drugs or treatment methods known to prevent the development of post-traumatic osteoarthritis (PTOA), a type of osteoarthritis (OA) that is triggered by traumatic joint injuries and accounts for ~12% of the nearly 600 million OA cases worldwide. Lack of effective drug delivery techniques remains a major challenge in developing clinically effective treatments, but cationic delivery carriers can help overcome this challenge. Scaling up treatments that are effective in in vitro models to achieve success in preclinical in vivo models and clinical trials is also a challenging problem in the field. Here we use a cationic green fluorescent protein (GFP) as a carrier to deliver Insulin-Like Growth Factor 1 (IGF-1), a drug considered as a potential therapeutic for PTOA. GFP-IGF-1 conjugates were first synthesized as fusion proteins with different polypeptide linkers, and their transport properties were characterized in human cartilage explants. In vitro experimental data were used to develop a predictive mathematical transport model that was validated using an independent in vitro experimental data set. In this study, the model was used to predict the transport of these fusion proteins upon intra-articular injection into human knee joints. The predictions included results for the rate and extent of fusion protein penetration into cartilage, and the maximum levels of fusion proteins that would escape into systemic circulation through the joint capsule. Together, our transport measurements and model set the stage for translation of such explant culture studies to in vivo preclinical studies and potentially clinical application.

36 MATERIALS SCIENCE↗