The Department of Energy has identified science and technology challenges of national importance to advance the Genesis Mission and accelerate innovation and discovery through artificial intelligence.
These challenges span DOE’s core missions in energy dominance, discovery science, and national security, and are designed to deliver measurable benefits for the American people. Each challenge was selected for its potential to harness the Genesis Mission’s AI platforms, world-class facilities, and public-private partnerships to drive scientific breakthroughs and real-world impact.
The Genesis Mission will accelerate the design and deployment of safe, advanced reactor technologies—including modular nuclear reactors—and fusion power sources to provide abundant, affordable, and resilient energy. The initiative will also modernize America’s electric grid.
The Genesis Mission will use advanced, physics-aware AI to accelerate scientific discovery. Scientists will create new breakthroughs in materials and chemistry, deepen our understanding of the universe, and help develop new quantum algorithms.
The Genesis Mission will allow the National Nuclear Security Administration (NNSA) to create and apply advanced AI technologies focused on all aspects of its national security mission. These systems will ensure the safety and reliability of the U.S. nuclear stockpile and accelerate the development of defense-ready materials.
Featured Challenges
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The electric grid faces reliability challenges and infrastructure limitations to accommodate dramatic increases in electricity demand from data centers, manufacturing, and electrification while maintaining affordable power for Americans.June 2, 2026 -
AI will revolutionize the entire critical minerals supply chain and development of alternative materials by integrating geophysical data, other fundamental science data, process optimization, cost estimation, and economic modeling into one connected system.June 26, 2026
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The convergence of current and emerging AI technology with the growing availability of large, curated datasets may be a tipping point for materials discovery, design, and qualification.April 20, 2026
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AI will integrate and interpret genomics, multi-omics, imaging, dynamics, and phenomics data into embedded models to establish genotype–phenotype relationships, identify causal control points, and guide autonomous experimentation.June 26, 2026
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Recent advancements in agentic and generative AI present opportunities to accelerate discovery and translational science.June 26, 2026
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Water availability is essential for expanding production and utilization of energy, as well as the nation’s health and security.March 31, 2026
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Novel AI could discover new quantum algorithms by automating and optimizing their design and translating them into applications without requiring prior domain knowledge.June 26, 2026
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Automating at least some parts of the scientific experimental scheme will both increase the volume of data produced for improved AI models and improve the repeatability of experiments.June 26, 2026
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Building construction faces high costs and long timelines due to fragmented permitting and poor data interoperability.March 31, 2026
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Microelectronics powers all aspects of our lives, including AI, but America faces intense global competition in critical microelectronics applications, including ultra-efficient semiconductors for AI computing, power electronics, and communication networks.June 2, 2026
Aligned Actions from the National Laboratories
National Challenge: Discovering Quantum Algorithms with AI
Leading Lab: Pacific Northwest National LaboratoryQuantum computers have the potential to dramatically accelerate complex calculations, but inefficient data preparation remains a major bottleneck to achieving quantum advantage. To meet this national challenge, Pacific Northwest National Laboratory developed “Picasso,” an AI-enabled parallel algorithm that uses classical computing to efficiently prepare data for quantum systems. By leveraging AI-augmented graph coloring to optimize how quantum workloads are grouped, Picasso can process problems of unprecedented scale—handling millions of Pauli strings in minutes and one trillion-plus relationships while using modest GPU memory. This advance significantly expands the class of problems that can be explored with quantum algorithms, accelerating progress toward practical quantum computing.
National Challenge: Securing America’s Critical Minerals Supply
Leading Lab: Idaho National LaboratoryPOC: Lionel TobaGlobalized trade has dramatically increased supply chain complexity, limiting the effectiveness of traditional analytics and slowing the nation’s ability to anticipate disruptions in critical materials. To address this national challenge, the Idaho National Laboratory is leading the development of FORESIGHT, an AI-enabled supply chain analysis capability. It applies big data, machine learning and advanced reasoning to identify emerging risks and predict potential disruptions across critical material value chains. By uncovering correlations, dependencies and causal relationships among supply chain events and entities, FORESIGHT enables rapid, scenario-based analysis that can be updated continuously as new information becomes available. This AI-driven approach reduces analysis timelines from months to days, providing policymakers, defense stakeholders and industry leaders with timely, actionable insights to proactively manage risks and strengthen U.S. supply chain resilience.
National Challenge: Scaling the Biotechnology Revolution and Achieving AI Driven Autonomous Laboratories
Leading Lab: Pacific Northwest National LaboratoryBiomanufacturing is a critical driver of U.S. economic competitiveness and energy independence, yet bringing novel bio-based fuels, chemicals, and materials to market remains slow and resource-intensive. To address this national challenge, Pacific Northwest National Laboratory has advanced the opensource BacterAI platform to optimize microorganisms central to bioproduction. By integrating reinforcement learning from BacterAI with laboratory automation, scientists enable continuous, closed-loop experimentation that rapidly explores large biological parameter spaces and identifies limits to microbial growth. This AI-driven approach dramatically increases experimental throughput—issuing new experiments in minutes rather than days—and generates comprehensive, AI-ready datasets that accelerate the commercialization of emerging biotechnologies.
National Challenges: Reenvisioning Advanced Manufacturing and Industrial Productivity; Securing U.S. Leadership in Data Centers; Achieving AI Driven Autonomous Laboratories
Leading Lab: Argonne National LaboratoryFuel combustion technologies underpin the majority of U.S. primary energy production and form the backbone of key industrial sectors. Yet, designing next generation combustion systems remains slow, expensive, and computationally constrained. Argonne National Laboratory, along with other national laboratories and industry partners, is addressing this challenge with COMB FLOW AI, a transformational frontier AI platform to dramatically accelerate discovery science and engineering for novel fuel combustion technologies. The platform comprises advanced scientific AI foundation models and agentic AI frameworks to enable fast, predictive digital twins for multiscale flow and combustion systems and orchestrate end to end design and optimization workflows. By enabling near real time forecasting, early detection of safety-critical extreme combustion events, and rapid co design of fuels and engines, COMB FLOW AI can significantly shorten design cycles and reduce maintenance costs—representing a significant improvement in how advanced energy technologies are developed and deployed.
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Under Secretary for Science Darío Gil serves as DOE’s director for the Genesis Mission. Subscribe to the office’s newsletter or follow Dr. Gil on social media for Genesis Mission updates.