USC has been selected to support the U.S. Department of Energy’s Genesis Mission—an effort that brings universities, industry, and 17 national laboratories together to accelerate AI-driven scientific discovery. This funding selection highlights USC’s expanding role in building an “AI-enabled scientific ecosystem” designed to make research faster, more predictive, and more cost- and time-efficient.
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
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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
