The Genesis Mission was created through Executive Order 14363 in November 2025 with the goal of doubling American scientific productivity within a decade. Its proposed infrastructure links the Department of Energy’s 17 national laboratories with some of the nation’s most powerful supercomputers, advanced AI models, quantum platforms and experimental facilities. The first funding round totals $293 million and represents the initial commitment toward an estimated $5 billion investment. Rather than treating artificial intelligence as a stand-alone computational tool, the initiative is designed to place AI inside scientific workflows, where algorithms can help formulate hypotheses, control experiments, identify physical patterns and optimize technologies that are too complex to manage through conventional methods.

One of the OU-led projects is focused on enhanced geothermal systems, a technology that could substantially expand access to reliable, low-carbon electricity. Ahmad Ghassemi, a McCasland Chair Professor of petroleum engineering in OU’s Mewbourne College of Earth and Energy, will lead the project “AI-Enabled Rapid Analysis and Control of EGS Stimulation Using Machine Learning & Physics-Based Hydraulic Fracture/Natural Fracture Interaction Modeling.” Jie Cao of OU’s School of Computer Science and Hao Hu of the School of Geosciences will support the effort. The project has received $734,129 in Phase I funding and is aimed at improving the way engineers create underground heat reservoirs by combining machine learning with detailed models of rock deformation and fracture behavior.

Enhanced geothermal systems are designed to extract heat from hot rock formations that do not naturally contain enough permeability for fluid circulation. Engineers inject fluid deep underground to open or reactivate fractures, creating pathways through which water can move, absorb heat and return to the surface. The process is difficult to control because the subsurface is heterogeneous: rock layers vary in strength, existing fractures can redirect fluid, and small changes in pressure may produce unexpected fracture networks. Ghassemi’s team will use AI to interpret data generated during stimulation while physics-based hydraulic-fracture and natural-fracture models constrain the algorithms. This hybrid approach could allow operators to distinguish useful permeability from unwanted fluid leakage and adjust injection strategies in near real time. The Department of Energy estimates that enhanced geothermal systems could eventually provide approximately 90 gigawatts of capacity nationwide.

“Plentiful energy can become available from enhanced geothermal systems,” Ghassemi said. “The key is fracturing rocks at great depths to create permeability, which experience has shown to be a challenging, complex process.” By integrating geomechanics, fracture mechanics and machine learning, the project seeks to turn underground reservoir creation from a largely empirical procedure into a more predictable and controllable engineering discipline. If successful, the system could help reduce drilling risk, improve the longevity of geothermal wells and make it possible to develop resources in regions previously considered unsuitable for conventional geothermal power. Because geothermal plants can operate continuously, unlike intermittent wind and solar facilities, improved subsurface control could also strengthen the reliability of future energy networks.

The second OU-led award is directed toward quantum computing. Grant Biedermann, Homer L. Dodge Endowed Chair and associate professor of physics in OU’s Dodge Family College of Arts and Sciences, will lead “AI-Driven Control and Optimization of QuDit Platforms in Rydberg Arrays.” The project has been awarded $651,891. It will investigate how artificial intelligence can control arrays of individually trapped atoms, each of which can serve as a quantum information element with more than two accessible states. These systems are known as qudits, in contrast to conventional qubits, which encode information using two states. A qudit can potentially store more information per physical element, although controlling its many energy levels introduces additional technical challenges.

The platform uses Rydberg atoms, whose outermost electrons are excited to very high-energy states. In this condition, atoms become extremely sensitive to one another over comparatively long distances, allowing researchers to engineer interactions that can implement quantum logic. Optical tweezers—tightly focused laser beams—hold individual atoms in precisely arranged arrays. Yet the same sensitivity that makes Rydberg systems powerful also makes them difficult to operate. Laser intensity fluctuations, atom loss, imperfect positioning, unwanted interactions and decoherence can all degrade performance. Biedermann’s team plans to use AI as a real-time control layer that can learn from measurements, predict system behavior and optimize experimental parameters faster than a human operator or a fixed control sequence.

The work connects to the Genesis Mission challenge “Discovering Quantum Algorithms with AI” and brings together researchers from OU, Oklahoma State University and Los Alamos National Laboratory. Biedermann said the collaboration includes Los Alamos scientists Martin, Zlotnik and Meier, as well as Bilitewski of Oklahoma State University, whose research focuses on atom-based qudit simulation. The broader objective is not simply to make quantum hardware run more efficiently, but to create a feedback loop between algorithms and physical devices. AI could search for quantum operations suited to the specific imperfections of an experimental platform, while the hardware could provide data that guides the design of new algorithms. Such adaptive control may become essential as quantum processors grow in size and complexity.

A third OU scientist, Xuguang Wang, Robert Lowery Chair Professor and Presidential Research Professor in the School of Meteorology, is participating in a project led by Lawrence Livermore National Laboratory. The project, “Scalable Twin for Intelligent Turbulence and Cloud Heuristics,” or STITCH, will use AI to model turbulence and clouds for Earth-system prediction. Turbulence occurs across a vast range of scales, from microscopic eddies to atmospheric systems spanning kilometers, while clouds form, evolve and dissipate through tightly coupled interactions involving moisture, radiation, temperature and airflow. Because numerical weather and climate models cannot resolve every relevant process directly, they rely on parameterizations—approximations that represent unresolved physics. STITCH aims to develop more intelligent and scalable methods for these difficult calculations.

The project addresses the Genesis Mission challenge “Predicting U.S. Water for Energy,” an issue with direct consequences for the national power system. Water availability influences hydropower production and affects the cooling systems used by thermal and nuclear power plants. More accurate predictions of clouds, precipitation and atmospheric turbulence could help energy operators plan for changing water supplies, extreme weather and regional demand. The combined OU projects illustrate how the Genesis Mission is connecting disciplines that are often treated separately: petroleum engineering with computer science, atomic physics with machine learning, and meteorology with energy planning. OU Vice President for Research and Partnerships Matt Hulver said the university’s researchers were selected because of the strength and relevance of their work, emphasizing that energy dominance, discovery science and national security are deeply interconnected.

Together, the projects represent a shift toward AI-augmented science in which algorithms do more than analyze completed experiments. In geothermal engineering, machine learning may help steer physical processes kilometers beneath the surface. In quantum computing, it may continuously tune lasers and atomic interactions while a processor operates. In atmospheric science, it may learn improved representations of physical processes that conventional models cannot fully resolve. The central challenge will be ensuring that AI systems remain scientifically reliable, interpretable and stable when confronted with conditions outside their training data. By combining data-driven methods with physical laws, laboratory measurements and high-performance computing, the OU teams hope to build systems that are not only faster, but also capable of producing discoveries and technologies that can be tested in the real world.

Subject of Research: Artificial intelligence applications in enhanced geothermal systems, quantum computing with Rydberg atom qudit arrays, and AI-based turbulence and cloud modeling for Earth-system prediction.

Article Title: University of Oklahoma Teams Bring AI to Geothermal Energy, Quantum Computing and Climate Prediction

Web References: https://www.ou.edu/mcee/mpge/people/faculty/ahmad-ghassemi ; https://www.ou.edu/cas/physics-astronomy/people/directory/faculty/grant-biedermann ; https://www.ou.edu/ags/meteorology/people/faculty/xuguang-wang ; https://www.energy.gov/undersecretaryforscience/genesis-mission/genesis-mission ; https://www.energy.gov/sites/default/files/2026-07/GM-RFA-Awards-List.pdf

References: University of Oklahoma; U.S. Department of Energy Genesis Mission; Lawrence Livermore National Laboratory; Los Alamos National Laboratory.

Image Credits: Travis Caperton/University of Oklahoma

Keywords

Artificial intelligence, Genesis Mission, University of Oklahoma, enhanced geothermal systems, geothermal energy, quantum computing, Rydberg atoms, qudits, machine learning, turbulence modeling, cloud prediction, Earth-system science, Department of Energy, discovery science, energy technology

Six USC-linked projects were chosen for Genesis Mission funding this week, including three led directly by USC researchers. Collectively, the teams target major bottlenecks across energy, aviation, manufacturing, and computing: reducing energy usage in computation, improving access to critical materials, and enabling AI tools to solve problems that traditional modeling struggles to address at scale.

In one project, USC Viterbi electrical and computer engineering professor Massoud Pedram is pursuing Fast Phase Logic, a superconducting computing approach aimed at delivering higher performance with dramatically lower power demands. Instead of relying on bulky wiring structures used in earlier superconducting designs, Fast Phase Logic replaces those elements with compact, engineered components. The tighter integration allows more circuit elements to fit within the same chip area and lowers the operating current—steps that improve practicality and scalability.

Superconducting systems require extreme cooling to achieve near-zero electrical resistance. Yet designing such chips is difficult because the physics is complex and trial-and-error cycles can take years. Pedram’s team uses AI to predict component behavior and automates chip layout decisions, compressing design timelines while exploring architectures that could benefit edge computing, data-stream processing, and future quantum-control systems.

Another USC-led effort focuses on critical minerals. Karen Lloyd of USC Dornsife is developing AI models to infer where and how critical minerals become concentrated deep underground. The research integrates geological signals, underground fluid chemistry, naturally occurring gases, and microbial genomics—combining datasets in ways that can reveal patterns difficult to detect using conventional methods alone.

By merging geophysical imaging with subsurface modeling and AI, the project aims to improve estimates of mineral occurrence in geothermal systems and clarify the conditions that shape mineral distributions. The work brings together expertise from USC and partner institutions including Woods Hole Oceanographic Institution, the University of North Carolina at Chapel Hill, Oak Ridge National Laboratory, the University of Tennessee, Knoxville, and Georgia Tech.

A third project, led by USC Viterbi aerospace and mechanical engineering associate professor Iván Bermejo-Moreno, seeks to teach AI to predict turbulence using physical laws rather than relying solely on traditional mathematical turbulence models. Working with the University of Michigan and Argonne National Laboratory, the team trains AI models to recognize recurring flow structures—enabling faster and more accurate simulations of airflow and other complex physical processes.

If successful, the approach could support better design of aircraft, wind turbines, and engines, while broadening the use of physics-informed AI across science and engineering workflows.

Additional USC participation extends the Genesis Mission footprint through subaward and partner roles in three more projects, ranging from AI-ready excited-state materials datasets to multi-agent discovery frameworks for selective recovery from battery waste, and from hardware-speed decoding methods to accelerated scientific modeling pipelines.

Subject of Research: AI for scientific discovery (superconducting chips, turbulence prediction, critical mineral sourcing)
Article Title: USC Selected for DOE Genesis Mission AI Research Funding
News Publication Date:
Web References: https://today.usc.edu/usc-leads-national-ai-research-project-to-accelerate-scientific-discovery/ ; https://genesis.energy.gov/
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Keywords: AI, superconducting computing, Fast Phase Logic, turbulence prediction, physics-informed machine learning, critical minerals, geothermal systems, geophysical imaging, multi-agent AI, DOE Genesis Mission