The Genesis Mission includes a portfolio of National Science and Technology Challenges that brings together the capabilities of the American Science and Security Platform and Federal agencies to accelerate American leadership in science, energy, national security, health, space, and more.
These challenges represent high-impact national priorities where advances in artificial intelligence, advanced computing, experimentation, and data can dramatically shorten discovery timelines, strengthen U.S. competitiveness, and deliver measurable benefits for the American people.
Featured Challenges
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The Nation cannot yet trace how the environments in which Americans live, together with lifestyle and genetic variation, drive disease across the life course.August 26, 2026 -
We need a way to integrate the disparate experimental results with theoretical knowledge to accelerate discovery.October 26, 2026 -
Nuclear power plants 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 centers.September 3, 2026 -
New AI models and advanced simulations can reduce costs, accelerate the discovery of next-generation construction materials and improve the design, operation, and longevity of the Nation's physical infrastructure.August 26, 2026 -
Superiority in space begins with the ability to see, predict, and safely maneuver within a domain that is growing more crowded, more contested, and more dynamic by the year.August 26, 2026 -
Transforming accelerator-based facilities into highly efficient, autonomous, and more productive capabilities requires a tight integration of AI with design and operation.August 26, 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.October 26, 2026 -
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.August 21, 2026 -
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.August 21, 2026 -
Recent advancements in agentic and generative AI present opportunities to accelerate discovery and translational science.August 21, 2026 -
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.October 26, 2026 -
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.August 21, 2026 -
Water availability is essential for expanding production and utilization of energy, as well as the nation’s health and security.October 26, 2026 -
Novel AI could discover new quantum algorithms by automating and optimizing their design and translating them into applications without requiring prior domain knowledge.August 21, 2026 -
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.August 21, 2026 -
Building construction faces high costs and long timelines due to fragmented permitting and poor data interoperability.August 21, 2026
Aligned Actions from the National Laboratories
National Challenge: Delivering Nuclear Energy that is Faster, Safer, Cheaper
Leading Lab: Idaho National LaboratoryTo quadruple nuclear capacity by 2050, the pace of deploying reactors will need to accelerate significantly beyond today's timeline. The Prometheus project, led by Idaho National Laboratory in partnership with Oak Ridge, Argonne, and Sandia national laboratories, universities, and commercial partners, is helping compress that timeline by applying agentic AI across the reactor development process.
AI agents coordinate engineering, licensing, manufacturing, and construction workflows, running physics-based simulations grounded in validated nuclear codes. The system draws on decades of curated nuclear research data and learns from each project as it progresses, giving it the context to iterate at speed.
The program targets a tenfold acceleration in design and licensing workflows and a threefold acceleration in manufacturing, laying the foundation for the industry to halve deployment timelines and reduce operating costs by 50 percent. Faster, cheaper deployment of nuclear energy positions the United States to meet surging energy demand and lead the next generation of nuclear reactors.
Thorsten Hellert, Berkeley Lab staff scientist and AI Genesis Mission MOAT co-PI, adjusts controls at the Advanced Light Source. (Credit: Thor Swift/ Berkeley Lab. © The Regents of the University of California, LBNL)National Challenge: Enhancing Particle Accelerators for Discovery
Leading Lab: Lawrence Berkeley National LaboratoryParticle accelerators are strategic infrastructure for science, industry, and medicine—and in the age of AI, how we design and operate them must evolve. To meet this challenge, Lawrence Berkeley National Laboratory created Osprey, an agentic AI platform that streamlines access to accelerator controls and synthesizes information at unprecedented scale. Already installed at eight facilities through the Berkeley Lab-led Multi-Office Accelerator Team (MOAT) project, Osprey is now being co-developed as a community tool by seven national laboratories.
Once fully deployed, Osprey will unlock access to millions of machine parameters and deliver operator queries more than 100 times faster. It will support everything from AI-assisted to fully autonomous operations, including preventive fault detection and automated recovery.
Integrated into the American Science Cloud, Osprey will let researchers across the USA treat the nation's accelerators as a single, intelligent system—dramatically speeding discovery, industrial and medical applications, and the development of next-generation accelerators with higher energy and more precise beams.
National Challenge: Enhancing Particle Accelerators for Discovery
Leading Lab: Thomas Jefferson National Accelerator FacilityPowerful research particle accelerators provide insight into our visible universe, but these sophisticated machines are complex to operate and maintain at peak efficiency.
To meet this national challenge, Thomas Jefferson National Accelerator Facility is applying AI to identify and predict faults before interruptions occur, to reduce damaging radiation, and to balance system components to prevent anomalies. These advances are increasing the uptime and reliability of research particle accelerators, ensuring they operate at peak efficiency now and in the future.
This research is also helping to transform the design of next-generation machines for applications and improvements in medical care, materials science, and energy production.
The Linac Coherent Light Source (LCLS) X-ray laser delivers 93 kHz —almost 100,000 pulses per second. Experiments at higher pulse rates capture ultrafast processes with greater precision and collect data more efficiently. (Jacqueline Ramseyer Orrell/SLAC)National Challenge: Enhancing Particle Accelerators for Discovery
Leading Lab: SLAC National Accelerator LaboratoryParticle accelerators are large, complex systems that power scientific discovery, and AI has the potential to improve their performance, reliability, and scientific throughput. To address this national challenge, SLAC National Accelerator Laboratory is integrating agentic AI workflows into accelerator control systems, enabling progress in autonomous accelerator tuning and complex diagnostic analysis.
Alongside this, SLAC is using AI/ML system modeling, continual learning, and digital twin capabilities, leveraging real-time integration between high-performance computing and the Linac Coherent Light Source (LCLS) to enable faster prediction of beam behavior and, in turn, aid rapid beam customization. These AI-enabled capabilities have improved prediction speeds for digital twins by more than one million times, enabled orders-of-magnitude faster beam customization, and improved key experiment characteristics, such as beam emittance and acceleration efficiency.
These advances will enable higher-quality beams produced on-demand for scientific and commercial applications, spanning computer chip manufacturing to medicine, while accelerating discovery.
Elias Nakouzi, a materials scientist at Pacific Northwest National Laboratory, leads research using artificial intelligence and automated experimentation to accelerate the recovery of critical materials from complex feedstocks.National Challenge:Securing America’s Critical Minerals Supply
Leading Lab: Pacific Northwest National LaboratoryRecovering critical materials from complex waste streams and other feedstocks can be difficult and time-consuming, limiting the ability to develop new domestic sources of materials needed for energy technologies.
Pacific Northwest National Laboratory (PNNL) is developing an agentic AI-guided workflow that autonomously plans, evaluates, and improves selective precipitation processes for critical materials recovery. Built on PNNL’s SciLink multi-agent automation platform, the closed-loop system uses AI to learn from experimental results and identify more effective separation conditions.
The approach has recovered high-purity magnesium from produced water, samarium from samarium-cobalt magnets, and rare earth elements from neodymium-iron-boron magnets with high yields. By automating and accelerating process development, the workflow can shorten the search for efficient, adaptable, and scalable separation methods from months or years to days.
National Challenge: Designing Materials with Predictable Functionality
Leading Lab: Sandia National LaboratoriesDensity Functional Theory is a gold standard for calculating the electronic-structure of materials, but its computational demands limit the size of practical simulations to only a few hundred atoms. Sandia National Laboratories and the Center for Advanced Systems Understanding (CASUS) developed Materials Learning Algorithms, or MALA, a machine-learning software framework that predicts materials’ electronic structure with chemical accuracy while scaling computationally with system size. By combining machine learning with physics-based calculations, MALA enables accurate electronic-structure modeling at length and time scales that were previously impractical. Researchers have demonstrated calculations involving more than 100,000 atoms and achieved more than a 5,000-fold speedup over Density Functional Theory for 2,000-atom systems. These capabilities open the door to faster, larger-scale simulations that can help researchers design and understand materials with predictable functionality.
MALA was recognized with a 2023 R&D 100 Award. Its award-winning models, data sets, and workflows will be included in Genesis Mission’s American Science and Security Platform, making this established capability available to future platform users.
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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.