One day after the launch of the Artemis 2 mission to the moon – the first in over half a century – the Department of Energy announced the launch of the Genesis Mission – a bold initiative to transform how the U.S. conducts scientific research and engineering. By democratizing access to advanced computing capabilities, the initiative could unlock new levels of scientific discovery and innovation that benefit the entire country.
The White House says the Genesis Mission has the potential to dramatically accelerate breakthroughs in fusion energy, power grid optimization, and materials science. The mission is built on three pillars – a computing platform for accelerating discovery, a portfolio of national challenges to serve as proving grounds, and a university engagement effort to rethink STEM education.
First announced last November, the mission is led by DOE Under Secretary for Science Dr. Dario Gil. President Trump lured Gil away from a 22-year career at IBM, where he had risen to senior vice president and director of IBM Research. Gil says the Genesis Mission “is building something like ‘an internet’ of science. It’s an intelligence layer connecting all the scientific instruments, laboratories, and universities into a seamless ecosystem for discovery.”
To kick off the Genesis Mission, the DOE announced a $293 million Request for Application (RFA), “The Genesis Mission: Transforming Science and Energy with AI.” The agency invited interdisciplinary teams to leverage novel AI models and frameworks to address 26 national challenges spanning advanced manufacturing, biotechnology, critical materials, nuclear energy, and quantum information science.
The RFA is open to teams from DOE National Laboratories, U.S. industry, and academia. Phase I awards range from $500,000 to $750,000 for a 9-month project period. Phase II awards range from $6 million to $15 million over a 3-year project period. Phase I applications and Phase II letters of intent are due April 28, and Phase II applications are due May 19. Successful AI models and workflows may be integrated into the American Science Cloud.
In concert with the mission, the DOE, in collaboration with Idaho National Laboratory INL), Argonne National Lab (ANL), Microsoft, and Everstar, used AI mapping to convert a safety analysis document needed as part of the Nuclear Regulatory Commission’s licensing process.
The team utilized Everstar’s Gordian AI solution, built on the Microsoft Azure platform, to convert the Preliminary Documented Safety Analysis for DOE’s National Reactor Innovation Center’s Generic High Temperature Gas Reactor into sections equivalent to an NRC license application.
The 208-page final document took just a single day to generate using the AI tool, whereas heretofore it took a team of people 4 to 6 weeks to complete the same task. As lagniappe, the AI tool also comprehensively identified missing or incomplete information needed to successfully complete an NRC application.
The next step toward full implementation is for a reviewing agent to evaluate the AI-generated documents against NRC guidance to validate they are ready for formal submittal. The team is also developing a benchmarking rubric to provide a confidence grade for the Gordian AI’s performance.
This project follows an earlier collaboration between INL and Microsoft to deploy an Azure AI-based solution to show how advanced AI models can generate engineering and safety analysis reports.
A recent NRIC study highlighted how AI has the potential to reduce both document development time and regulatory review cycles by up to 50% while also improving accuracy, consistency, and traceability. Using these and other AI tools therefore has the potential to dramatically lower the cost – and shorten the time — for nuclear reactor permitting.
These and other hoped-for breakthroughs come under the auspices of the “Delivering Nuclear Energy That is Faster, Safer, Cheaper Challenge” under the Genesis Mission.
The DOE is using a suite of explainable AI solutions, including surrogate models, agentic workflows, autonomous labs, and digital twins to meet its goal of cutting both timeframes and operational costs for nuclear energy deployment at least by half – for design, licensing, manufacturing, construction, and operation.
Fusion energy is another prime example of how AI can compress timelines. Up till now, developing high-performance computing simulation codes that match real-world observations take weeks or even months to run at the desired level of fidelity.
But by training neural networks on the output of these validated simulations, researchers can produce AI-based models that issue predictions up to tens of thousands of times faster. In sum, AI tools may bring fusion energy much closer to reality than “30 years away.”
According to Gil, some practical applications of the Genesis Mission involve the nation’s electric grids. According to grid operators, 80% to 90% of developer interconnection applications are deficient. Should the DOE Office of Electricity’s AI-agentic framework, now under development, become operational, applicants could identify and correct errors before submitting applications and thus allow interconnection studies to begin up to a year earlier.
Brookhaven National Laboratory is building an AI emulator called Grid FM that can accelerate power flow calculations by a factor of 100. For the Texas transmission grid (ERCOT) there are 2,000 nodes, more than a thousand potential connection points, 4,000 contingencies, and 10 different 24-hour load scenarios at 5-minute increments – adding up to roughly 10 billion power flow simulations. What would take 20 years with conventional methods can be completed using Grid FM in about 2 months.
Nuclear-related challenges also include several related to nuclear weapons, nuclear energy research, and cleanup and restoration of nuclear reactor sites. Other challenges go far beyond nuclear energy and grid security, including reenvisioning advanced manufacturing and industrial productivity, reimagining construction and operation of buildings, scaling the biotechnology revolution, and securing America’s critical minerals supply.
The Genesis Mission will also address discovering quantum algorithms with AI, realizing quantum systems for discovery, recentering microelectronics in America, securing U.S. leadership in data centers, predicting U.S. water for energy, unleashing subsurface energy assets, designing materials with predictable functionality, achieving AI-driven autonomous laboratories, and accelerating materials discovery, production, and qualification for strategic deterrence.
Like the Artemis mission, the Genesis Mission is part of President Trump’s grand strategy to restore American dominance and stimulate the U.S. economy through a revival of manufacturing and global leadership across the sciences.
While the Artemis mission has captured global attention, Gil promises astounding results from the Genesis Mission, boasting that “We ain’t seen nothing yet!”
Duggan Flanakin is a senior policy analyst at the Committee For A Constructive Tomorrow who writes on a wide variety of public policy issues.
This article was originally published by RealClearEnergy and made available via RealClearWire.
