USC is among the nation’s leading research universities selected to help advance the U.S. Department of Energy’s Genesis Mission , a national initiative that unites universities and industry with the country’s 17 national laboratories to explore AI for scientific discovery.
This week, six projects involving USC researchers, including three led by USC, were selected to receive Genesis Mission funding.
Together, these six projects tackle some of the most pressing challenges facing the industries that power modern life, from energy and aviation to manufacturing and computing. Their research aims to enable faster, more energy-efficient computing, strengthen supplies of critical materials and develop AI-driven tools that help scientists solve problems too complex, costly or time-consuming for conventional approaches.
“This outstanding achievement is a testament to the caliber of USC’s researchers and the growing momentum of our research enterprise,” said Ishwar K. Puri , USC’s senior vice president of research and innovation.
“We are proud to help lead the national effort to build an AI-enabled scientific ecosystem, advancing bold ideas and interdisciplinary collaborations that will shape the future of scientific discovery,” he said.
Every time we stream a video, use generative AI or connect another smart device, computers must process more data while consuming as little energy as possible. Yet today’s computer chips are reaching their practical limits, making it increasingly difficult to deliver higher performance without dramatically increasing power consumption.
Massoud Pedram , the Charles Lee Powell Chair in Electrical and Computer Engineering at the USC Viterbi School of Engineering, is using AI to develop a new kind of computer chip that could be both faster and far more energy-efficient.
Supported by the Genesis Mission, the project is exploring a promising alternative called Fast Phase Logic, a type of superconducting electronic, that replaces the bulky wire components used in earlier superconducting designs with much smaller, specially engineered components. The design allows many more circuit elements to be packed into a given space and reduces the amount of electrical current the chip needs to operate, making the technology more practical to build and scale up.
The chips rely on superconductors — materials that, when cooled to extremely low temperatures, let electricity flow with almost no resistance. However, designing these types of chips typically takes years of trial and error due to the complex physics involved.
USC researchers believe AI can help speed up the process.
Pedram’s team is using AI to predict how the chip components will behave and to automate the layout process, cutting down design time dramatically. If successful, their work could pave the way for future computing hardware that could support high-speed edge computing, streaming data processing and the control systems needed for quantum computers.
The minerals that power modern technology — from superconductors to the batteries that power everyday products like phones and electric vehicles — are in short supply and becoming harder to find.
Karen Lloyd , USC Wrigley Chair in Environmental Studies at the USC Dornsife College of Letters, Arts and Sciences, will lead a Genesis Mission-funded project that uses AI to better understand where and how these critical minerals are naturally concentrated deep beneath Earth’s surface.
The research combines clues from geology, underground water, naturally occurring gases and microorganisms to build a more complete picture of the processes that concentrate critical minerals. By bringing these diverse datasets together, AI can help researchers identify patterns that would be difficult to detect using traditional methods alone, allowing scientists to focus future field studies on the most promising areas.
The team will integrate geophysical imaging, underground fluid chemistry, microbial genomics and computer modeling into AI models that improve estimates of where critical minerals are likely to occur and deepen scientists’ understanding of the conditions that shape their distribution in geothermal systems.
The project brings together researchers from USC; Woods Hole Oceanographic Institution; the University of North Carolina at Chapel Hill; Oak Ridge National Laboratory; the University of Tennessee, Knoxville; and the Georgia Institute of Technology.
Iván Bermejo-Moreno , associate professor of aerospace and mechanical engineering at USC Viterbi, will develop a new AI trained on the laws of physics to more accurately predict turbulence, one of the most difficult and computationally demanding problems in engineering.
Working with the University of Michigan and Argonne National Laboratory, researchers are training AI to recognize recurring flow structures in turbulent systems rather than relying solely on traditional mathematical models, enabling faster and more accurate simulations of airflow, energy systems, weather and other complex physical processes.
The project has the potential to improve the design of aircraft, wind turbines, engines and other critical technologies while expanding AI applications to broader scientific challenges.
In addition to the three awards led by USC, the university also serves as a partner and subawardee on three additional projects:
The Genesis Mission is a historic national initiative led by the U.S. Department of Energy, which is building the world’s most powerful integrated science discovery platform. By uniting government, industry, academia, and philanthropy, it is accelerating breakthroughs in energy, scientific discovery, and national security through a new platform that combines AI, supercomputing, quantum systems, and advanced scientific instruments.
The goal of the Phase I RFA awards is to identify promising pathways toward transformative scientific capabilities and establish a foundation for future investment and scale. Project teams will design and demonstrate research workflows that integrate AI with scientific investigation, while rigorously evaluating whether those approaches can accelerate discovery, improve predictive capabilities, enhance experimentation, or generate new scientific insights.