The US Department of Energy (DOE) has announced 26 science and technology challenges of national importance to advance the Genesis Mission and accelerate innovation and discovery through artificial intelligence (AI).
The Genesis Mission, launched in November 2025, is a DOE-led national initiative designed to accelerate scientific discovery by integrating AI with federal research capabilities. It aims to double scientific productivity within a decade by creating a unified AI-enabled platform linking supercomputers, national labs, and private sector partners to advance fields such as biotechnology, energy, and material science. This comes shortly after DOE announced the Genesis Mission Consortium, a public-private partnership administered by TechWerx, to drive collaboration and innovation.
In line with President Trump’s November 2025 Executive Order (EO) Launching The Genesis Mission and January 2025 EO Removing Barriers to American Leadership In Artificial Intelligence, the challenges span DOE’s discovery science, energy, and national security missions. Each was selected for its potential to deliver measurable benefits for the American people and to accelerate advancements through the Genesis Mission’s AI platforms, world-class facilities, and public-private partnerships.
“These challenges represent a bold step toward a future where science moves at the speed of imagination because of AI. It’s a game-changer for science, energy, and national security,” said DOE Under Secretary for Science and Genesis Mission Lead Dr Darío Gil. “By uniting the US Government’s unparalleled data resources and DOE’s experimental facilities with cutting-edge AI, we can unlock discoveries that will power the economy, secure our energy future, and keep America at the forefront of global innovation.”
Michael Kratsios, Assistant to the President and Director of The White House Office of Science and Technology Policy, noted: These 26 challenges are a direct call to action to America’s researchers and innovators to join the Genesis Mission and deliver science and technology breakthroughs that will benefit the American people. We look forward to expanding the list of challenges across Federal agencies to bring even greater impact to the Mission.”
The 29-page document listing the challenges describes the AI solution, its justification, and the national impact for each of them. The 26 challenges include seven that relate the civil nuclear development and three concerned with nuclear weapons development and deterrence. The others relate to transmission planning, interconnection processing, data centre integration, and water-resource forecasting. These are areas where modelling speed and system uncertainty are constraining infrastructure development. To tackle these problems, DOE will deploy AI-driven analytics and simulation tools to accelerate decision-making, improve reliability, and support large-load expansion.
The civil nuclear challenges are: Delivering Nuclear Energy that is Faster, Safer, Cheaper; Accelerating Delivery of Fusion Energy; Transforming Nuclear Cleanup and Restoration; Harnessing America’s Historic Nuclear Data and Research; Increasing Experimental Capacity at Nuclear Research Facilities; Safeguarding Nuclear Materials from Proliferation Threats; and Streamlining Production, Removing Red Tape, and Ensuring Safety in the Nuclear Enterprise.
Delivering Nuclear Energy that is Faster, Safer, Cheaper. DOE says NPPs “have historically been challenged by long development timelines and burgeoning costs, limiting America’s ability to deliver affordable, resilient, and reliable energy as demand continues to grow particularly from AI data centres”.
The AI Solution will accelerate nuclear energy deployment by using AI to design, licence, manufacture, construct, and operate reactors with human-in-the-loop workflows, enabling at least two times schedule acceleration and greater than 50% operational cost reductions. “To meet these goals, we are using a suite of explainable AI solutions including surrogate models, agentic workflows, autonomous labs, and digitals twins.”
DOE’s says its combination of national laboratory nuclear expertise, test facilities, decades of operational data, regulatory partnerships, industry partners, and extensive computational ecosystem uniquely position it to accelerate reactor deployment.
Accelerating Delivery of Fusion Energy Challenge: Realising fusion energy on the grid requires coordinated progress across six tightly coupled challenge areas defined in the Fusion Science and Technology Roadmap. Isolated, device specific trial-and-error approaches cannot manage these interdependencies at the scale, complexity, or pace required to meet national energy objectives.
The AI Solution enables physics-constrained digital twins that integrate plasma, nuclear, materials, and system behaviour within a unified predictive framework, allowing performance and engineering trade-offs, failure modes, and design margins to be evaluated consistently in simulation and experiment. An AI-Fusion Digital Convergence Platform (DCP) will integrate novel algorithms in HPC codes, foundation models for plasma and materials science, physics- and chemistry-informed neural networks, surrogate models, and digital twins for whole-facility modelling and real-time control across the six Roadmap challenge areas. The DCP will accelerate infrastructure development, shorten innovation cycles, and support a competitive US fusion ecosystem.
DOE says it uniquely brings together fusion facilities, national laboratories, leadership-class computing, data stewardship, and public-private partnerships to build and operate a trusted, national-scale AI platform that integrates data, models, and experiments across the fusion ecosystem. That platform will leverage large-scale domestic and international fusion facilities and fusion materials and technology infrastructure across both public and private sectors to meet fusion roadmap milestones.
Transforming Nuclear Cleanup and Restoration. DOE’s environmental cleanup mission faces an estimated $540bn liability over eight decades with some 90m gallons of highly radioactive tank waste requiring treatment that impedes site remediation and restoration crucial for American energy, security, and innovation.
The AI Solution is “a multimodal AI foundation model will be trained on DOE Office of Energy Management’s (EM) unparalleled 30+ years of operational data from unique nuclear processing facilities to predict scale-dependent behaviour across lab, pilot, and full-scale systems”. National laboratory experts will leverage Equinox supercomputing capabilities for accelerated simulation architecture in development of the AI models. The goal is to use AI to enable mission acceleration to meet EM’s 2040 vision with significant liability reduction.
DOE says EM’s unique data assets from designing and operating large-scale facilities at complex sites combined with the capabilities provided by the industry partners, will enable development of scale-bridging AI models that safely and efficiently address deployment challenges no other institutions can leverage.
Harnessing America’s Historic Nuclear Data and Research. The US possesses a vast and unparalleled archive of classified scientific experiments and nuclear weapon tests, as well as unclassified historical work in nuclear science that spans more than eight decades. However, much of this vital information exists only in written notes, printed materials, or photographs, and has yet to be digitised and adequately labelled, hindering its accessibility and usability for continued research, design, and production. “This challenge addresses the urgent need to modernise historical information and convert it into usable data for enhanced security and research by AI tools.”
The AI Solution is to build an AI digitisation-and-reconstruction pipeline (optical character recognition + vision + information extraction + 3D/geometry inference) that converts analogue reports, imagery, and drawings into searchable, simulation-ready datasets, including automated meshing and cross-referencing to historic test outcomes. “To accelerate progress, we must establish a secure centralised archive with durable metadata/ontology standards, triage workflows that prioritise the highest-value records, and end-to-end access controls and quality assurance processes that function across classification levels.”
DOE says the project is critical for preserving valuable historical records and enhancing data management capabilities. It will preserve eight decades of at-risk physical records before degradation or loss, leverage the National Nuclear Security Administration’s (NNSA’s) secure computing, classification expertise, and specialised archives, and cut manual cataloguing effort by an order of magnitude ensuring consistent data quality. “Finally, it establishes a compliant, scalable system for ongoing data ingestion and governance.”
Increasing Experimental Capacity at Nuclear Research Facilities. The NNSA is confronted with increasing demand for optimised experiments, conducted at limited-capacity facilities. Current processes to design, document, and analyse experiments are often slow and inefficient, leading to backlogs in experimental campaigns and reduced facility capacity.
TheAI Solution is to deliver an AI “facility operating system” that uses agentic workflows to plan/schedule experiments, steer execution in real time, and fuse live diagnostics with multi-fidelity simulation so each shot/test yields maximum information with minimal turnaround. To accelerate progress, we must build interoperable facility digital twins and streaming data/provenance standards, plus transparent approval gates, audit logs, and uncertainty-aware analytics that operators can trust in high-consequence environments.
DOE says NNSA’s integration of advanced technologies and resources uniquely positions it to address important challenges in nuclear security. NNSA combines limited-capacity, high consequence experimental facilities and test sites with supercomputing, advanced simulation, and automated labs under a secure framework, enabling real-time inference by uniting diverse physics models, uncertainty quantification, and robotic execution. “This produces a scalable, reusable architecture (facility models, data standards, and assurance methods) that meets strict safety, security, and quality controls.”
Safeguarding Nuclear Materials from Proliferation Threats. Preventing nuclear materials and information from falling into the hands of “rogue and dangerous actors” (non-proliferation) is central to America’s national security. Against the backdrop of a global surge in demand for civilian nuclear power, the challenge of monitoring and enforcing non-proliferation commitments is growing rapidly. To effectively investigate and prosecute potential threats, we integrate vast amounts of complex data from diverse sources using AI. “This challenge will develop and deploy advanced AI technologies that can analyse these growing data streams in real time, identifying anomalies that indicate potential proliferation activities.”
The AI Solution is to develop multimodal foundation models and analytic agents that fuse satellite imagery, sensing, open-source, and government data to detect subtle proliferation-relevant anomalies in near real time and generate analyst-ready evidence packages with confidence scoring. “To accelerate progress, we must build secure multi-network data-sharing and governance, domain ontologies and knowledge graphs for the fuel cycle, continuous testing and red-teaming, and human-AI teaming interfaces that scale across classified and unclassified environments.”
DOE says the project leverages NNSA’s unique capabilities to address the challenges of modern data analysis in non-proliferation efforts. NNSA is the only organisation with the nuclear fuel cycle domain expertise required to aggregate global non-proliferation-relevant data sources. “Manual analysis cannot keep pace with the volume and variety of modern data streams; automated, trustworthy AI shortens detection timelines, increases efficacy of detection activities, and improves use of technical subject matter experts. This effort will leverage existing and planned NNSA platforms and partnerships for rapid deployment.”
Streamlining Production, Removing Red Tape, and Ensuring Safety in the Nuclear Enterprise. “Not all challenges to our nuclear deterrence are external; some are directly within our own walls.” Current regulatory processes in high-hazard facilities are slow and fragmented, leading to inefficiencies that can compromise safety and operational effectiveness. “These mostly well-intended policies are no longer compatible with the urgency of NNSA’s modernisation and production mission. This challenge is significant as it directly impacts the ability to respond to nuclear threats as well as to maintain safety standards efficiently. AI-driven facility and process models can optimise operations with real-time data to streamline schedules, allocate resources, and eliminate bottlenecks.
The AI Solution is to deploy auditable, policy-grounded AI (large language models + agents) that can digest safety-basis requirements, automate safety analyses and documentation, and continuously generate risk-aware work plans while autonomously configuring and running large simulation campaigns. “To move faster safely, we must establish a trusted digital regulatory corpus with provenance, develop verification and testing harnesses for AI outputs, and integrate these tools into facility/operations data systems with robust access controls and end-to-end audit logs.”
DOE says the project is designed to enhance safety and efficiency by providing transparent, auditable AI solutions that support expert oversight in critical operations. It transforms compliance into a strategic advantage. “By digitising and AI-enabling regulatory documents in situ, we create a verifiable, fully auditable record of every search, analysis, and decision, turning safety basis navigation from a bottleneck into a transparent trust-building capability. The effort ensures data-driven efficiency, unifying engineering, safety, and operational data to cut planning and documentation time by over 50%. Finally, it leverages existing strengths by building on secure data networks and in-house expertise to rapidly deploy and validate advanced AI tools.”
The defence related challenges are: Accelerating Nuclear Threat Assessment, Preparedness, and Response; Integrating Design and Production Operations for Nuclear Deterrence; and Strengthening Deterrence Through Attribution of Nuclear and Radiological Signatures.
Accelerating Nuclear Threat Assessment, Preparedness, and Response. Rapid and effective response to nuclear and radiological events demands detailed analysis of vast and varied data, from radiation detectors and environmental sensors to intelligence reports and simulation outputs. “Today, our Nuclear Emergency Support Teams (NEST) rely on manual review and static AI models that cannot sufficiently adapt as new data streams in, creating delays in detection, assessment, and response. This leaves decision-makers without real-time situational awareness or clear risk metrics when addressing threat assessments and event response.”
The AI Solution is to deploy a continuously learning, multimodal AI fusion system that integrates radiation/environmental sensors, simulations, and intelligence reporting to deliver real-time decision support with scenario modelling and uncertainty-aware risk metrics on crisis timelines. “To accelerate progress, we must develop cross-classification data governance and trusted deployment patterns (edge-capable compute, rigorous red-teaming, and auditable human-approval workflows) so the system can be used operationally without sacrificing reliability”
DOE says that NNSA uniquely combines nuclear science expertise, secure facilities, and High-Performance Computing (HPC) resources. “This effort will embed physics-informed models for transparent, auditable AI in mission-critical environments, replace brittle, hand-tuned workflows with a single scalable solution cutting development time and cost, and leverage existing archives and platforms for rapid deployment and scaling.”
Integrating Design and Production Operations for Nuclear Deterrence. “Deterring America’s adversaries from using strategic weapons has never been more urgent or more complex. Answering that threat requires greater flexibility and innovation in our nuclear weapons production capabilities.” The current handoff of weapons systems between Design Agencies (DAs) and Production Agencies (PAs) is slow and inefficient, hindering our deterrence mission. By leveraging multiscale physics, digital twins, and AI capabilities, NNSA will enhance decision-making, reduce rework, and improve production efficiency to provide new weapons systems to warfighters in a fraction of the traditional time and cost.
The AI Solution is to deliver an AI-powered nuclear security enterprise twin “that couples physics constrained surrogates, production digital twins, and agentic workflows to co-optimise designs and manufacturing in a closed loop, dramatically shrinking DA/PA iteration time while preserving traceability and decision quality.” To accelerate progress, we must build secure cross-site data inter-operability (schemas, provenance, and governance), validated and uncertainty-aware models, and human-in-the-loop orchestration with auditable controls that work across required classification environments.
DOE says this initiative is particularly suited to the NNSA because it leverages secure access to extensive historical design and production data, HPC capabilities for developing and validating advanced models, and reliable AI systems that can function across various network classifications. “The result will be nothing short of a revolution in weapons design and production. By creating an AI-integrated Certification Environment, NNSA can shift from traditional document-based processes to agile, model-driven decision-making.
Strengthening Deterrence Through Attribution of Nuclear and Radiological Signatures. Every nuclear material has a signature. Through a combination of radiological and material characterisation analysis, the provenance of loose or employed nuclear materials (like with a weapon of mass destruction or a dirty bomb) can be positively determined. “Deterrence is only possible if the very real threat of American retaliation can hold adversaries accountable for their actions…. This effort will dramatically improve the speed and accuracy of nuclear forensics analyses by removing uncertainty or anonymity from the misuse of nuclear materials”. This initiative aims to leverage AI to drastically accelerate nuclear forensics in off-normal, uncontrolled, and novel environments.
TheAI Solution is field multimodal forensic AI (vision + spectroscopy + morphology + inverse modelling) that can rapidly characterise samples and debris, infer likely process history/origin, and support device/material reconstruction while enabling portable, on-site triage tools. “To accelerate progress, we must build curated and shareable signature libraries with ground-truth links, standardized lab-to-field workflows with calibration and uncertainty quantification, and validated deployment pipelines that preserve chain-of-custody, auditability, and expert oversight.”
DOE says the project capitalises on NNSA’s unique resources and expertise to enhance the speed and accuracy of nuclear threat attribution. It builds on NNSA’s unique combination of nuclear test archives, specialised labs, and expert staff, eliminates current multi-day bottlenecks in sample processing and reporting, resulting in more efficient use of existing resources… and leverages existing infrastructure investment for rapid implementation without compromising quality.
DOE Acting Under Secretary of Energy Alex Fitzsimmons said: “AI has tremendous potential to amplify the Trump Administration’s mission to unleash American energy dominance.” DOE Under Secretary for Nuclear Security Brandon Williams said: “By leveraging AI across our missions – from modernizing our nuclear deterrent to safeguarding critical infrastructure – we will ensure America remains at the forefront of global security and technological leadership.”
