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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Towards intelligent emergency control for large-scale power systems: Convergence of learning, physics, computing and control

Here, this paper has delved into the pressing need for intelligent emergency control in large-scale power systems, which are experiencing significant transformations and are operating closer to their limits with more uncertainties. Learning-based control methods are promising and have shown effectiveness for intelligent power system control. However, when they are applied to large-scale power systems, there are multifaceted challenges such as scalability, adaptiveness, and security posed by the complex power system landscape, which demand comprehensive solutions. The paper first proposes and instantiates a convergence framework for integrating power systems physics, machine learning, advanced computing, and grid control to realize intelligent grid control at a large scale. Our developed methods and platform based on the convergence framework have been applied to a large (more than 3000 buses) Texas power system, and tested with 56 000 scenarios. Our work achieved a 26% reduction in load shedding on average and outperformed existing rule-based control in 99.7% of the test scenarios. The results demonstrated the potential of the proposed convergence framework and DRL-based intelligent control for the future grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Supporting ARPA-E Power Grid Optimization (Final Report)

Pacific Northwest National Laboratory (PNNL), Arizona State University (ASU), Georgia Institute of Technology (Georgia Tech), Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), The University of Texas at Austin (UT), and the University of Wisconsin-Madison (UW-M) supported the ARPA-E Grid Optimization (GO) Competition by providing a common problem formulation, data format, datasets, evaluation mechanism, scoring, rules, and results that resulted in the awarding of $\$9.24$ million dollars to teams from academia, industry, and national labs for solving three sets of increasingly difficult non-linear, security- constrained AC Optimal Powerflow (AC-OPF) optimization problems in order to increase the efficiency of the US Electric Grid. It is estimated that a 1% increase in efficiency can save $\$1$ billion. Current industry practices typically use a linear DC model (DC-OPF) in order solve the OPF problem within the time constraints of the operation schedule. The GO Competition challenges the best power engineers, mathematicians, and computer scientists to make possible operational decisions based on accurate physical models. To accomplish this, the GO Competition created a series of Challenges and funded teams to produce the best solver. Challenge 1 was to solve the security constrained Alternating Current Optimal Power Flow (ACOPF) problem. Challenge 2 extended that to by adding adjustable transformer tap ratios, phase shifting transformers, switchable shunts, price-responsive demand, ramp rate constrained generators and loads, and fast-start unit commitment (UC). Furthermore, Challenge 2 was a maximization problem while Challenge 1 was a minimization problem. While Challenge 3 was being developed, the entrants were invited to find better solutions to the Challenge 2 synthetic datasets with no restrictions on time, hardware, or algorithms. The Challenge 2 solutions turned out to be very good. Challenge 3 expanded the Challenge 2 problem further by using multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead. These problems included active bid-in demand and topology optimization. Together the Challenges used nearly 30 million CPU hours. Since each team was working on the same problem, using the same data, and running on the same hardware, fair comparisons could be drawn as to the best solver. The datasets were varied enough, however, that the best solver for one dataset was not necessarily the best at another, so cumulative scores were used. The process was managed by the PNNL maintained website https://GOCompetition.energy.gov, where Entrants could find information about the problem, the data, the rules, submit their solver for evaluation, and see the scores of all the competing teams on a Leaderboard. Interest was world-wide but only American teams were eligible for prizes. The Competition has produced 34 journal articles 115 papers and been cited over 500 times in the literature, including 12 dissertations (4 from foreign countries; Columbia (2), Germany, and Italy) and 3 from the DOE ExaScale project. Software developed by Pearl Street Technologies for Challenges 1 and 2 is now deployed by Southwest Power Pool (SPP) and Midcontinent Independent Service Operator (MISO). Other teams have received inquiries from venture capitalists. Google DeepMind has thanked the Competition for making the datasets developed for the Competition public. They are using it to train machine learning models. The larger datasets have billions of unknowns to be solved for, but only a small percent matter in the final solution. Knowing what unknowns are important can dramatically speedup the solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Verifying LLM generative agents reflect human behavior in contested information environments to effectively simulate disinformation campaigns (Proteus)

Disinformation poses a significant and evolving threat to today’s online environment. Individuals encounter challenges in detecting disinformation, subsequently influencing their behavior and decision-making processes. Our research examines the potential use of large language model (LLM) generative agents (LGAs) to replicate human behavior to better understand how disinformation is spread in online environments. Using human subjects research, we first investigate how personality traits, individual differences, and demographic factors relate to decision-making in simulated online disinformation environments. Then, we examine whether LGAs can effectively replicate human responses in the same simulated online environments when assigned personality traits, demographic characteristics and behavioral attributes. Our findings indicate that LGAs can align with human decisions in these scenarios; however, alignment is contingent upon scenario context, persona settings and LLM selection. Results provide valuable insights for methodology refinement in future research and in utilizing LGAs to model complex national security challenges such as disinformation campaigns.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Systems-To-Atoms (S2A): enabling hydrogen for climate security

The project addresses a critical gap in hydrogen infrastructure by integrating system-level energy models with atomic-scale material simulations in a unified Systems-to-Atoms (S2A) framework. The motivation stems from the need to develop efficient, cost-effective, and durable hydrogen transport and utilization technologies to support decarbonization of hard-to-electrify sectors such as heavy-duty transportation. Current system models lack awareness of material performance mechanisms, while material-scale models do not account for system-level usage and variability. To bridge this divide, the team developed a co-simulation capability linking techno-economic analyses, reactor/process-flow modeling, and molecular-scale catalysis simulations. Applied to hydrogen delivery in California, the framework enabled comparative evaluations of compressed, cryogenic, and liquid organic hydrogen carrier (LOHC) pathways, highlighting how catalyst operation and unit process efficiency influence overall performance. The results demonstrate that no single material or transport mode is universally optimal; instead, heterogeneous solutions tuned to specific operational contexts deliver better performance. The project delivers a new capability for cross-scale material co-design, advancing hydrogen infrastructure readiness and informing DOE and LLNL missions in climate and energy resilience.

organic↗
DOE OSTI2025

Cybersecurity Considerations for Hydrogen Infrastructure in Airport Environments

This report explores key cybersecurity concerns and best practices within environments that serve as reference points for the development of hydrogen fueling infrastructure for aviation. This cybersecurity analysis leverages prior NREL studies: 1) hydrogen fueling station component validation to identify vulnerabilities and failure events documented in physical equipment, and 2) electric aircraft charging infrastructure analysis to explore primary cybersecurity vulnerabilities. It reviews the criticality of digitized technologies in sustaining hydrogen fuel production, storage, and fueling systems, noting cybersecurity concerns that are universal to power systems and industrial control systems in general. In considering cybersecurity vulnerabilities within a future landscape of hydrogen energy for aviation applications, a reference architecture was intended to reveal the points of connection between assets and the potential sensors that are vulnerable to manipulation in the event of compromised access or communication within a SCADA system. A generalized reference architecture can help stakeholders, engineers, or strategists understand connections, criticalities, and standard practices when it comes to designing and planning for new systems. There are several gaps to account for in assessing the future of hydrogen production, storage, and fueling for aviation. Engaging stakeholders, including aircraft manufacturers, electric utilities, site property owners, and local communities, will inform decision-making around site structure, operations, and resources for future hydrogen fueling infrastructure to understand operational needs and cybersecurity awareness. Cybersecurity mitigation strategy must consider physical attack vectors that emerge with the integration of hydrogen systems into existing airport security requirements. The cybersecurity risk assessment contained in this report is an entry point into potential future granular-level analyses to be conducted as part of hazard and risk assessments for safe aviation hydrogen infrastructure, determining how the scale of hydrogen fuel infrastructure for aviation impacts the volume of cyber attack vectors, and what, if any, are the vulnerabilities associated with different types of on-board hydrogen systems. In this nascent development phase, assessing how best to integrate cybersecurity practices into an evolving U.S. aviation landscape provides critical insights into building increased awareness and stakeholder engagement to support a cyber-resilient infrastructure.

08 HYDROGEN↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Relative sensitivity of plastic scintillator: A comparative analysis with 60 Co gamma rays, deuterium–deuterium, and deuterium–tritium neutrons

A plastic scintillator has found extensive application in the realm of high-energy physics and national security science. Many applications in those fields often involve the simultaneous production of photons, neutrons, and charged particles, which makes the relative sensitivity information for these different radiation types important. In this study, we have adopted a multi-head detector comprised of a plastic scintillator and high gain phototubes, which provides a large dynamic range and linearity. A comparative study on the relative sensitivities of plastic scintillators was facilitated by adopting three distinct radiation calibration sources (i.e., 60 Co γ rays, DD neutrons, and DT neutrons). Neutrons from a DD source generate a comparable level of scintillation to gamma rays emitted by 60 Co (i.e., 60 Co-γ/DD-n = 0.92 ± 16%). DT neutrons induce ~3.5 times the scintillation observed with DD neutrons (i.e., DT-n/DD-n = 3.5 ± 28%). In addition, the Geant4 simulation granted us valuable insights into the relative sensitivity of the scintillator. This comparative study will provide a useful database for users in diverse applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Deploying Adversarial Attacks in Super-Resolution Models

Reliable super-resolution methods are crucial for applications like remote sensing, grid resilience and disaster impact analysis, and standoff biometrics. These methods infuse additional high-frequency information into reconstructions, allowing for better contextualization and image intelligence. However, super-resolution models can also introduce hallucinations or other unseen vulnerabilities that could be exploited by an adversary. This is further compounded by the prominence of deep learning in these models, as models are often blindly applied on out-of-distribution images. In this work, we implement adversarial attacks in common open-source super-resolution models and examine their impact on reconstructions and downstream classification tasks. We find that an adversarially trained super-resolution model can produce high-quality reconstructions that degrade downstream classifications. Moreover, these attacks do not require access to low-resolution imagery or class labels at inference time. These results demonstrate the vulnerability of super-resolution methods to malicious actors and motivates the development of a detector for super-resolution adversarial attacks. Further exploration of adversarial attacks in this domain is required to ensure trustworthiness and robustness of super-resolution models for national security applications.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Deep Cyber-Physical Situational Awareness for Energy Systems: A Secure Foundation for Next-Generation Energy Management

This document provides the final report for the CYPRES project. The purpose is (1) to highlight and summarize its major accomplishments and (2) to provide guidance on how its outcomes have informed and can inform important additional research and technology transfer. The goal of CYPRES was the research, development, and demonstration of a security-oriented next generation cyber-physical EMS for electric power systems that detects malicious and abnormal events through the fusion of cyber and physical data. To achieve this, the CYPRES project team researched, developed, and built a prototype of the solution, referred to as the CYPRES EMS. The CYPRES EMS is a proof-of-concept cyber-physical platform that demonstrates the management of the energy system, communications, security, and cyber-physical grid modeling and analytics. As part of the capabilities of the CYPRES EMS, the team designed and developed a suite of power system applications for monitoring, risk analyses, detection, and control that are inherently cyberaware. At its core, the project aimed to research, develop, and demonstrate a security-oriented next-generation cyber-physical Energy Management System (EMS) capable of detecting malicious and abnormal events through the innovative fusion of cyber and physical data. This approach represents a fundamental shift from traditional EMS, reimagining how critical infrastructure can be protected through unified cyber-aware and physics-aware secure data flow pipelines. The project’s cornerstone deliverable, the CYPRES EMS, serves as a proof-of-concept cyber-physical platform that revolutionizes the management of energy systems, communications, security, and cyber-physical grid modeling and analytics. This prototype implements a comprehensive suite of power system applications for monitoring, risk analyses, detection, and control, all designed with inherent cyber awareness. The system’s architecture extends from end-devices in the field through to control center applications, establishing a secure and resilient control framework that addresses the challenges posed by diverse devices of unknown trustworthiness connecting to modern power systems. Through this innovative approach to deep cyber-physical situational awareness, the CYPRES project not only advances the state-of-the-art in energy infrastructure protection but also establishes a new paradigm for how EMS can be designed, deployed, and operated in an increasingly complex threat landscape. The findings and developments from this project provide crucial insights for stakeholders across the energy sector, offering a blueprint for enhancing the reliability and resilience of our nation’s critical energy infrastructure in the face of evolving cyber threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Cyber-Physical Tabletop Exercise for Small Modular Reactor Facilities

U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping costs reasonable to make nuclear energy competitive. This evolving threat landscape includes adversaries having offensive cyber capabilities to attack information technology (IT) systems and operation technology (OT) systems. These adversaries may have the ability to attack the physical protection system (PPS) networks with potential consequential impacts that could degrade the effectiveness of the PPS. These cyber attacks may also be used to attack the safety and operational systems used to operate and ensure the safety of the reactor. Additionally, adversaries may gain access to unmanned aerial systems (UAS) that may be used to provide reconnaissance and surveillance of the facility, provide information to the adversaries, and be equipped with kinetic capabilities such as explosives or weapons that can be used to directly attack the facility. The Department of Energy’s Office of Nuclear Energy’s Advanced Reactor Safeguards and Security (ARSS) program funded Sandia National Laboratories (SNL) and Idaho National Laboratory (INL) to develop a cyber-physical tabletop exercise (TTX). This exercise was conducted on a hypothetical small modular reactor (SMR) facility, and only considered a potential adversary cyber attack on the PPS to a physical attack on the hypothetical facility to achieve a radiological release. This cyber-physical TTX is meant to provide lessons learned to integrate the cyber security system design and the physical protection system (PPS) design to decrease design, operation, and maintenance costs as well as increase effectiveness for defending against design basis threat attacks at the facility. This TTX will also provide a framework and method for SMR and microreactor vendors to conduct their own cyber-physical TTX and gain impactful insights to improving the cyber and physical protection system design for their SMR or microreactor facility design.

42 ENGINEERING↗
DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗
DOE OSTI2025

Securing Federated Learning Against Active Reconstruction Attacks

Federated Learning (FL) has amassed notable attention for its ability to preserve user privacy while emphasizing the retainment of model training efficiency. Due to this potential, FL has been integrated in many domains, such as healthcare, finance, law, and industrial engineering, where data cannot be easily exchanged due to sensitive information and strict privacy laws. However, current research has indicated that FL protocols are easily compromised by active data reconstruction attacks employed by actively dishonest servers. The malicious modification of global model parameters allows an actively dishonest server to obtain a direct copy of users’ private data via gradient inversion. Here, this class of attacks is highly underexplored and continues to be a major challenge due to the intense threat model. In this paper, we propose OASIS as a scalable and modality-agnostic defense based on data augmentation that counteracts active data reconstruction attacks while preserving model performance. To generalize our defense, we uncover the intuition behind gradient inversion that enables these attacks and theoretically establish the conditions by which the defense can be considered robust regardless of attack design. From this, we formulate our defense with data augmentation that illustrates its ability to undermine the attack principle. We evaluate OASIS on five real-world datasets–two image-based (ImageNet and CIFAR100) and three text-based (Wikitext, Stack Overflow, and Shakespeare)–which span diverse uses cases such as vision tasks and language modeling. Comprehensive evaluations on these datasets exhibit the efficacy of OASIS and highlight its feasibility as a solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Playing Nonlocal Games across a Topological Phase Transition on a Quantum Computer

Many-body quantum games provide a natural perspective on phases of matter in quantum hardware, crisply relating the quantum correlations inherent in phases of matter to the securing of quantum advantage at a device-oriented task. In this Letter, we introduce a family of multiplayer quantum games for which topologically ordered phases of matter are a resource yielding quantum advantage. Unlike previous examples, quantum advantage persists away from the exactly solvable point and is robust to arbitrary local perturbations, irrespective of system size. We demonstrate this robustness experimentally on Quantinuum’s H1-1 quantum computer by playing the game with a continuous family of randomly deformed toric code states that can be created with constant-depth circuits leveraging midcircuit measurements and unitary feedback. We are thus able to tune through a topological phase transition—witnessed by the loss of robust quantum advantage—on currently available quantum hardware. This behavior is contrasted with an analogous family of deformed Greenberger-Horne-Zeilinger states, for which arbitrarily weak local perturbations destroy quantum advantage in the thermodynamic limit. Lastly, we discuss a topological interpretation of the game, which leads to a natural generalization involving an arbitrary number of players.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature

Abstract Efficiently extracting data from tables in the scientific literature is pivotal for building large‐scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule‐based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT‐based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow‐up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero‐shot, few‐shot, and fine‐tuning, the study presents a Pareto‐front mapping where the few‐shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score >95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.

Yi, Gyeong Hoon [Computational Science Research Ce↗
DOE OSTI2024

Safety in Artificial Intelligence: Challenges and Opportunities for the U.S. National Labs and Beyond

This report discusses the importance of the critical and underexplored topic of artificial intelligence (AI) safety, as highlighted during the “Strategy Alignment on AI Safety” workshop convened at Lawrence Livermore National Laboratory (LLNL) in April 2024. Through a summary of keynote talks, panel discussions, and breakout sessions, world-leading AI safety experts from academic, industry, national labs, and government agencies clearly agree on the need for and importance of large-scale investments for research and capabilities in AI safety. With the field innovating at unprecedented rates, there is increasing urgency to develop novel evaluation methodologies that allow full considerations of risks/threats of AI technologies in different domains. Quantitative metrics and effective methodologies that can evaluate and audit the “safeness” of how a given AI technology is trained, deployed, or regulated are, at best, nascent for certain scenarios or, more commonly, nonexistent. This maturation gap presents the possibility of serious threats to national security, and further inaction may have serious consequences. Additionally, the gap between the public’s and research community’s perceptions of AI risks/rewards is significant. While numerous voices from the AI community have expressed concern that the risks could be so high that future AI systems could inflict extinction-level damage to humanity if deployed incorrectly, the public largely is aware only of risk in low-impact scenarios. This discrepancy highlights the crucial need for researchers to articulate to governmental bodies what, why, and when various AI risks matter as part of motivating funding requests. Thus, the call to action for this community is to pursue AI safety as a “Big Science” project on a scale comparable to the Manhattan Project. High risks and high payoffs are on the table, but safe AI is a fast-moving target, and large-scale investments are needed to guide development of this technology in a responsible way. We highlight the need for a multilayered solution combining the development of new methods and algorithmic approaches to mitigate threats with an active participation of the government(s) in setting high industry standards and regulations based on state-of-the-art technology. The U.S. Department of Energy (DOE) national laboratories have served as leading institutions for scientific innovation in the U.S. for more than 70 years. Drawing on their expertise in the AI community and their history of safeguarding critical and sensitive information, and as we look to the future, national labs are the best choice for evaluating and safeguarding AI technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Clean Energy Cybersecurity Accelerator: Cohort 2 - Asimily Public Report

The U.S. Department of Energy (DOE) Office of Cybersecurity, Energy Security, and Emergency Response (CESER) sponsors the Clean Energy Cybersecurity Accelerator (TM) (CECA) to expedite the deployment of emerging security technologies that address the most urgent security concerns facing modern and future electric grids. CECA Cohort 2 assessed solutions focused on hidden risks due to incomplete system visibility and device security and configuration. Improving visibility can be achieved through operational technology (OT) asset identification solutions, including capabilities like automatic discovery, vulnerability reporting, and configuration monitoring. Solutions that monitor and identify assets in information technology (IT) networks in other domains are widely used; however, there is far less adoption of monitoring solutions for operational technology environments. Wider adoption may increase with increased confidence in the ability for these solutions to understand and respond to the specific requirements of OT environments. CECA Cohort 2 evaluated the active and passive asset discovery capabilities of market-ready solutions, documented and analyzed results, and identified gaps in functionality or capabilities. This report and describes how these results can help advance the adoption of these and similar solutions in the electric sector.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Tactical Analysis for Calculating Contextual Risk at Boundaries: Summary of Laboratory Directed Research & Development Effort

The Tactical Analysis for Calculating Contextual Risk at Boundaries (TACCRAB) tool is an innovative digital twin (DT) platform and automated risk algorithm designed to transform operational decision-making in structured screening environments, with an initial focus on Southern Border Land Ports of Entry (POEs). The invention provides integration points for advanced artificial intelligence, predictive modeling, and real-time data analysis to produce a comprehensive risk management tool that enables proactive, data-informed security strategies. The core inventive features of TACCRAB center on its unique risk algorithm, which dynamically calculates contextual risk by synthesizing historical data, near real-time streaming data from the checkpoints themselves, and AI-generated predictions. Unlike traditional risk assessment methods, TACCRAB utilizes a DT to provide comprehensive operational insights, allowing stakeholders to visualize, simulate, and optimize checkpoint configurations with unprecedented speed and contextual awareness. TACCRAB's key innovation lies in its ability to combine multiple complex inputs - including technology detection probabilities, resource availability, screening pathway characteristics, and threat actor behavioral patterns - into a unified risk calculation and update these inputs based on changing operational and environmental conditions. By leveraging a DT that continuously updates and learns from linked data, TACCRAB can suggest adaptive mitigation strategies that minimize risk while maintaining operational efficiency. Particularly novel is the platform's approach to decision support, which goes beyond static risk assessment. The DT provides dynamic metrics such as wait times, resource allocation effectiveness, and potential emerging threat scenarios, enabling users to view sophisticated, relevant what-if simulations and optimize checkpoint operations in near real-time. The system's architecture allows for generalized application across different screening environments, such as secure facilities, ports of entry, and soft targets, making it a versatile tool for security and operational management. The invention distinguishes itself through its comprehensive integration of predictive modeling, AI-driven pattern discovery, and user-friendly interface design. By combining these elements, TACCRAB transforms complex risk data into actionable insights, supporting decision-makers at various organizational levels - from booth agents making split-second screening decisions to checkpoint managers optimizing the day's resource allocation to strategic planners managing long-term investments.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Certified randomness using a trapped-ion quantum processor

Although quantum computers can perform a wide range of practically important tasks beyond the abilities of classical computers, realizing this potential remains a challenge. An example is to use an untrusted remote device to generate random bits that can be certified to contain a certain amount of entropy. Certified randomness has many applications but is impossible to achieve solely by classical computation. Here we demonstrate the generation of certifiably random bits using the 56-qubit Quantinuum H2-1 trapped-ion quantum computer accessed over the Internet. Our protocol leverages the classical hardness of recent random circuit sampling demonstrations: a client generates quantum ‘challenge’ circuits using a small randomness seed, sends them to an untrusted quantum server to execute and verifies the results of the server. We analyse the security of our protocol against a restricted class of realistic near-term adversaries. Using classical verification with measured combined sustained performance of 1.1 × 10 18 floating-point operations per second across multiple supercomputers, we certify 71,313 bits of entropy under this restricted adversary and additional assumptions. Our results demonstrate a step towards the practical applicability of present-day quantum computers.

computer science↗