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DOE OSTI2026

Laboratory Directed Research and Development Program: FY 2025 Completed Projects

Oak Ridge National Laboratory (ORNL) is the US Department of Energy’s (DOE’s) largest multiprogram science, technology, and energy laboratory. It possesses distinctive capabilities in a variety of fields, such as neutron science, computing, advanced materials, and nuclear science and technology. Using these capabilities, ORNL conducts basic and applied research and development (R&D) to support DOE’s overarching mission “to ensure America’s security and prosperity by addressing its energy, environmental and nuclear challenges through transformative science and technology solutions.” As a national resource, ORNL also applies its capabilities and skills to the specific needs of other federal agencies and customers through the DOE Strategic Partnership Projects (SPP) Program. Information about the laboratory and its programs is available on the ORNL website.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

The state of the art for neutron irradiation experiments from the perspective of the High Flux Isotope Reactor (HFIR)

Irradiation experiment campaigns are critical to advancing nuclear energy technologies by providing data on material performance under relevant radiation conditions. Successful irradiation experiments require integrated design efforts that balance technical goals with facility constraints. Here, this paper presents an expert-informed overview of irradiation experiment design at the High Flux Isotope Reactor. It addresses the nuclear materials research and irradiation experiment communities to guide them toward developing technically sound, facility-compatible campaigns. The High Flux Isotope Reactor is a multipurpose reactor supporting isotope production, neutron scattering, and materials testing. Its high, steady-state neutron flux is ideal for irradiation experiments, but successful execution demands coordinated thermal, structural, and reactor physics analyses. The paper outlines the complete development workflow from concept definition and design optimization to safety qualification and post-irradiation examination. Standardized capsule platforms are also discussed in terms of flexibility, specimen capacity, and thermal performance. Common failure modes such as unanticipated geometric variations, can impact temperature-dose profiles and compromise data reliability. Therefore, detailed thermal modeling and accurate as-built characterization are essential for meaningful post-irradiation data interpretation. Key recommendations include early engagement all stakeholders, clearly defined design expectations, and alignment of specimen geometries with post-irradiation examination capabilities. This approach reduces design iterations, enhances data quality, and supports more efficient use of irradiation resources. Strategic and well-planned irradiation testing not only improves individual campaign success but also accelerates the deployment of advanced nuclear technologies. By closing critical data gaps and reducing development risks, the nuclear materials community can more effectively contribute to the future of clean, resilient energy systems.

Experiments↗

DOE OSTI2024

Automated electrosynthesis reaction mining with multimodal large language models (MLLMs)

Leveraging the chemical data available in legacy formats such as publications and patents is a significant challenge for the community. Automated reaction mining offers a promising solution to unleash this knowledge into a learnable digital form and therefore help expedite materials and reaction discovery. However, existing reaction mining toolkits are limited to single input modalities (text or images) and cannot effectively integrate heterogeneous data that is scattered across text, tables, and figures. In this work, we go beyond single input modalities and explore multimodal large language models (MLLMs) for the analysis of diverse data inputs for automated electrosynthesis reaction mining. We compiled a test dataset of 65 articles (MERMES-T24 set) and employed it to benchmark five prominent MLLMs against two critical tasks: (i) reaction diagram parsing and (ii) resolving cross-modality data interdependencies. The frontrunner MLLM achieved ≥96% accuracy in both tasks, with the strategic integration of single-shot visual prompts and image pre-processing techniques. We integrate this capability into a toolkit named MERMES (multimodal reaction mining pipeline for electrosynthesis). Our toolkit functions as an end-to-end MLLM-powered pipeline that integrates article retrieval, information extraction and multimodal analysis for streamlining and automating knowledge extraction. This work lays the groundwork for the increased utilization of MLLMs to accelerate the digitization of chemistry knowledge for data-driven research.

Leong, Shi Xuan↗

DOE OSTI2026

Enhancing the EVI-X National Framework to Address Emerging Questions on Charging Infrastructure Deployment

As national investments in EV charging infrastructure accelerate, this project advances the state-of-the-art in infrastructure analysis to better inform deployment strategies. It enhances NLR's EVI-X Suite, a set of tools supporting national network planning, local site design, and financial evaluation. These capabilities will position VTO to provide timely analysis to the DOT/DOE Joint Office, and to guide the strategic buildout of the national charging network. EVI-X development is closely coordinated with other VTO-funded efforts to ensure consistent, integrated use of key inputs and outputs, including EV adoption scenarios from TEMPO and infrastructure designs developed through EVs@Scale.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

A Review of Infrasound and Seismic Observations of Sample Return Capsules since the End of the Apollo Era in Anticipation of the OSIRIS-REx Arrival

Advancements in space exploration and sample return technology present a unique opportunity to leverage sample return capsules (SRCs) towards studying atmospheric entry of meteoroids and asteroids. Specifically engineered for the secure transport of valuable extraterrestrial samples from interplanetary space to Earth, SRCs offer unexpected benefits that reach beyond their intended purpose. As SRCs enter the Earth’s atmosphere at hypervelocity, they are analogous to naturally occurring meteoroids and thus, for all intents and purposes, can be considered artificial meteors. Furthermore, SRCs are capable of generating shockwaves upon reaching the lower transitional flow regime, and thus can be detected by strategically positioned geophysical instrumentation. NASA’s OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC is one of only a handful of artificial objects to re-enter the Earth’s atmosphere from interplanetary space since the end of the Apollo era and it will provide an unprecedented observational opportunity. This review summarizes past infrasound and seismic observational studies of SRC re-entries since the end of the Apollo era and presents their utility towards the better characterization of meteoroid flight through the atmosphere.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Towards physics-informed explainable machine learning and causal models for materials research

From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Active causal learning for decoding chemical complexities with targeted interventions

Abstract Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task—finding molecules with a large dipole moment—our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Analysis of Conservation Voltage Reduction under Inverter-Based VAR-Support [Slides]

Conservation voltage reduction (CVR) is a common technique used by utilities to strategically reduce demand during peak periods. As penetration levels of distributed generation (DG) continue to rise and advanced inverter capabilities become more common, it is unclear how the effectiveness of CVR will be impacted and how CVR interacts with advanced inverter functions. In this work, we investigated the mutual impacts of CVR and DG from photovoltaic (PV) systems (with and without autonomous Volt-VAR enabled). The analysis was conducted on an actual utility dataset, including a feeder model, measurement data from smart meters and intelligent reclosers, and metadata for more than 30 CVR events triggered by the utility over the year. The installed capacity of the modeled PV systems represented 66% of peak load, but reached instantaneous penetrations reached up to 2.5x the load consumption over the year. While the objectives of CVR and autonomous Volt-VAR are opposed to one another, this study found that their interactions were mostly inconsequential since the CVR events occurred when total PV output was low.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Community Energy Planning: Best Practices and Lessons Learned in NREL's Work with Communities

The clean energy transition is accelerating due to local, state, and national actions combined with external market forces. Regardless of where goals are set and decisions are made, clean energy deployment occurs on the ground in communities. As a result, communities increasingly need technical expertise and assistance with planning for and managing the energy transition. Building on decades of work with state, local, and tribal jurisdictions, the National Renewable Energy Laboratory (NREL) increasingly provides community-driven modeling, analysis, and technical assistance to enable more data-driven and equitable community energy planning. To inform and enhance NREL's capabilities in community energy planning and provide a resource for others working in this space, the Joint Institute for Strategic Energy Analysis (JISEA) Sustainable Communities Catalyzer supported this best-practices presentation based on interviews with seasoned NREL practitioners and a literature review on equitable community energy planning. Findings include five best practices for community energy planning that NREL practitioners and others can apply to strengthen their impact: 1) do your homework in preparation for community interactions; 2) be humble, authentic, and honest in your interactions with community members; 3) respect community agency in every step of the process; 4) meet the community where they are; 5) democratize participation.

best practices↗

DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

A Method for Assessing Effectiveness and Technical Capabilities Required for Integrated Deterrence (NSLP-Final Report-Draft2022)

The United States faces an ever-increasingly multi-polar security environment dominated by great power competition with China and a lingering Russian threat as well as from would-be regional hegemons led by ambitions from Iran and North Korea. Our adversaries are pursuing and expanding the strategic means by-which they have an asymmetric offset (e.g., cyber, space, and other modes below the level of armed conflict). To combat and deter against the widening range of hostile actions, the U.S. must have additional capabilities with-which to deter. All of which drives us towards a more thoughtful integrated approach to deterrence, attempting to best align deterrent tools with the adversary actions in order to maximize the credibility of our deterrent. Such an approach complicates our deterrence strategy and demands a methodology to assess whether the U.S. has the deterrent tools necessary to deter the adversarial actions most costly to the U.S. To address this complexity, we create a framework to comprehensively and systematically assess our deterrent tools as qualitatively measured against the adversarial actions we wish to deter. Deterrence is ultimately an operation in the cognitive domain and at the heart of the framework presented here is the fundamental deterrence calculus which we use as the defining measure of whether a tool will credibly deter a given action. We describe the eight levers of the deterrence calculus and distill these levers to four products that are used to qualitatively assess the overall effectiveness of deterrence tools against adversarial actions. Credibility is determined by two principal variables: the technical credibility of the deterrent tool, and the principle of proportionality. Technical credibility of realized deterrence products is assessed through development of key mission requirements and hardware (e.g., components) that will impose costs, deny benefits, or encourage restraint. The principle of proportionality qualitatively asserts that for a deterrent tool to be cognitively credible, the costs imposed against, or benefits denied by, an action are commensurate to the magnitude of costs received from the adversarial action. By basing this framework on these two fundamentals, it is possible to compare deterrent tools in a systematic approach across the broad spectrum of hostile actions.

42 ENGINEERING↗

DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗

DOE OSTI2022

Simultaneous flow of zero-carbon and conventional fuel liquids through Trans-Alaska Pipeline System (Abstract)

The purpose of this CRADA is to address key technical challenges specific to the transport of ammonia, a promising carbon-free fuel and hydrogen-carrier, through crude oil pipelines. Specifically, the project seeks to develop novel technologies for preparing liquid ammonia/ hydrocarbon mixtures for the dual purpose of (i) pipeline transport, and (ii) developing advanced marine fuel blends. By demonstrating compatibility with the Trans-Alaskan Pipeline System (TAPS) ammonia/oil blends may improve access to stranded natural gas and help overcome low-flow issues associated with declining oil productivity. In Alaska, this technology enhances the capabilities of TAPS allowing it to function as a statewide “Hydrogen Highway” for exporting green or blue ammonia. This is strategically important for Alaska which lacks statewide electrical transmission infrastructure but contains vast renewable and fossil resources located in remote regions with few local markets. Nationally, Alaska-sourced green (hydropower) or blue ammonia has the potential to improve reliability of a Pacific Northwest hydrogen storage hub as a hydrogen-carrier by helping to overcome seasonality of green hydrogen produced from solar or wind energy. Globally, this technology project has significant potential to improve the safety and efficacy of ammonia-rich fuel compositions for use in maritime propulsion and other mid-sized engines.

10 SYNTHETIC FUELS↗

DOE OSTI2024

Simultaneous flow of zero-carbon and conventional fuel liquids through Trans-Alaska Pipeline System [Abstract]

08 HYDROGEN↗

DOE OSTI2021

Brain Connectivity Workshop Series Report

The National Institutes of Health (NIH) BRAIN Initiative aims to accelerate development and application of technologies that show how individual brain cells in complex neural circuits interact at the speed of thought and action. Complete wiring diagrams of the mammalian brain will revolutionize the capabilities of researchers to formulate and test models of how activity within brain circuits drives coordinated function and behavior. A top priority for the BRAIN Initiative, motivated by the strategic guidance of the BRAIN 2.0 Working Group report, is to muster the resources and foster the collaborations needed to generate these wiring diagrams at the level of long-range projections (“projectomes”) and synapses (“connectomes”) in whole mammalian brains, including those from rodents, humans, and other large-brained mammals. Achieving the scale and resolution necessary to characterize and use these projectomes and connectomes will not only push the boundaries of current imaging and computational capabilities to generate faster, cheaper, and more scalable technologies, but also drive broad innovation in data science, artificial intelligence, and machine learning.

42 ENGINEERING↗

DOE OSTI2026

Interface-engineered (CrMnTiZnCo) 3 O 4 @polypyrrole nano-hybrids for superior lithium storage

High-entropy oxides (HEO) have emerged as promising anode materials for lithium-ion batteries (LIBs) due to their high theoretical specific capacity. However, their practical application is hindered by several challenges, including significant volume expansion, electrode pulverization, and substantial irreversible capacity loss during initial cycles. To address these limitations, this study designed a novel core-shell composite material, denoted as HEO@PPy, which consists of a (CrMnTiZnCo) 3 O 4 -based HEO core and a polypyrrole (PPy) shell. This composite demonstrates remarkable electrochemical performance: it maintains a specific capacity of 1090.1 mAh/g after 100 cycles at 100 mA/g and retains 521.8 mAh/g after 1000 cycles at 1 A/g, highlighting its superior cycling stability. Furthermore, it exhibits excellent rate capability, delivering a capacity of 372.1 mAh/g even at a high current density of 5 A/g. These findings confirm that the strategic compositional and structural design of HEOs, combined with hybridization with conductive polymers like PPy, provides a viable pathway for developing advanced anode materials for next-generation, high-performance lithium-ion batteries.

25 ENERGY STORAGE↗

DOE OSTI2020

Cyber Resilience as a Deterrence Strategy

This paper was written by the Cyber Deterrence and Resilience Strategic Initiative in partnership with the Resilience Energy Systems Strategic Initiative. Resilience and deterrence are both part of a comprehensive cyber strategy where tactics may overlap across defense, resilience, deterrence, and other strategic spaces. This paper explores how building resiliency in cyberspace can not only serve to strengthen the defender's posture and capabilities in a general sense but also deter adversaries from attacking.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Enabling Capabilities and Resources: 2024 Principal Investigator Meeting Proceedings

As a major supporter of basic genome-enabled research, BER’s Biological Systems Science Division (BSSD) fosters scientific discovery by funding - fundamental biological research across disciplines in conjunction with enabling investigational tools and computational capabilities that include world-class user facilities. The overarching goal of BSSD is to provide the necessary fundamental science to understand, predict, manipulate, and design biological systems that underpin innovations for bioenergy and bioproduct production and enhance understanding of natural, DOE-relevant environmental processes (Biological Systems Science Division Strategic Plan, 2021). To accelerate the U.S. bioeconomy, BSSD pursues innovative science underpinning advances in sustainable biofuels and bioproducts and the development of next-generation technologies and computational resources for systems biology research. The 2024 BSSD Enabling Capabilities and Resources (ECR) Principal Investigator (PI) meeting brought together PIs across the BSSD ECR portfolio to confer on shared interests and opportunities. The meeting was held concurrently with the Genomic Science program (GSP) PI meeting to optimize collaboration on research to advance bioenergy and the bioeconomy. Rick Stevens of Argonne National Laboratory gave a keynote on How Generative Artificial Intelligence Can Impact Biological Research (see Keynote: How Generative Artificial Intelligence Can Impact Biological Research, this page). Plenary presentations included several joint sessions that illuminated the integration and understanding of the larger BSSD mission. GSP’s objective is to provide systems-level understanding of plants, microbes, and their communities through its Bioenergy Research, Biosystems Design, and Environmental Microbiome Research portfolios. The objective of the ECR portfolio is to support development of computational and instrumental platforms to advance fundamental GSP research—and BER more broadly— toward the overall goal of understanding the functional principles of living systems and their response to environmental challenges.

59 BASIC BIOLOGICAL SCIENCES↗