Pending final funding agreements between lead organizations and DOE, Berkeley Lab is a partner on 32 additional Genesis Mission projects led by other national labs, universities, and companies. Learn more about these projects below.
Scaling the Biotechnology Revolution
- Predictive AI to Map Point Mutation Effects on Protein Function: Measurement and Biosynthesis of Isoprenoids – Led by Rice University
- AI-Driven Design of Gene Expression Programs in Plants Using Foundation Models and High-Throughput DNA Synthesis – Led by Stanford University
- Foundation Models for Metabolic Engineering – Led by University of Illinois
- Precision Microbiome Engineering in Anaerobic Communities: from Deconstruction to Bioproduction – Led by UC Santa Barbara
- Digital twins for cell free enzymatic cascades – Led by Invizyne
Enhancing Particle Accelerators for Discovery
- AI/ML Resonance control for high-reliability, low-cost accelerator operations – Led by Fermi National Accelerator Laboratory
- Self-Optimizing Digital Twin for Compact Laser Plasma Accelerator Driven Light Sources – Led by Inversion Semiconductor Inc.
- Digital Twins for Laser-Plasma Wakefield Acceleration: From Plasma Channel Formation to Beam Optimization – Led by Lawrence Livermore National Laboratory
- AI-Enabled Digital Twin for Near-Real-Time Optimization and Decision Support of Fusion NBI Systems – Led by Realta Fusion, Inc.
Unleashing Subsurface Strategic Energy Assets
- Transforming subsurface prediction with an autonomous, multi-agent AI workflow for multi-scale coupled bio-hydrogeochemical modeling – Led by University of Arizona
- AI-enabled subsurface biogeochemical modeling – Led by Oak Ridge National Laboratory
- AI-Guided Map for Biomining of Critical Minerals (AIM-BioCM): Integrating Microbial, Hydrological, and Geochemical Data – Led by Texas A&M University
- Generative AI-Enabled Digital Twins for Subsurface Fracture Systems with Active Learning Multiphysics Data Assimilation – Led by the University of Minnesota
Predicting U.S. Water for Energy
- RAIN: Rigorous AI and Information Network for Surface-Subsurface Water System Coupling – Led by EnviTrace LLC
- Multi-Fidelity AI Foundation Model for Coupled Surface-Groundwater Predictions of Water Availability and Flood Hazards Across CONUS – Led by Argonne National Laboratory
- MIRACLE-AI: Molecular-to-Reservoir Integrated AI for Coupled Landscape and Earth-system Subsurface Intelligence – Led by Sandia National Laboratory
Securing America’s Critical Minerals Supply
- AI-driven discovery of electrochemical separation methods for rare earth elements – Led by Massachusetts Institute of Technology
- AI-Driven Discovery of Rare Earth Separation Chemistry – Led by Riven Corporation
- AI-Driven Probabilistic Source Mapping for Critical Minerals at Continental Scale – Kent State University
Reenvisioning Advanced Manufacturing & Industrial Productivity
- AI-Enabled Electrochemical Refinery – Led by California Institute of Technology
- AI-accelerated digital twin for REBCO coated conductor manufacturing in superconducting fusion magnet systems – Led by Florida State University
Accelerating Delivery of Fusion Energy
- Autonomous Implosion Design and Experimental Co-piloting via Physics-Grounded Agentic AI – Led by University of Rochester
- Facility-Level Agentic AI for Portable LaserNetUS Diagnostics and Interoperable Device Integration – Led by Lawrence Livermore National Laboratory
Realizing Quantum Systems for Discovery
- Causal AI for Deterministic Quantum Emitters from Atoms to Photonic Systems – Led by UC Irvine
- Denoising Quantum Sensors: Agentic Algorithm Creation and Real-Time AI for Quantum-Enabled Rare Event Discovery – Led by UC San Diego
Scaling the Grid to Power the American Economy
- GRID OPS AI: Scaling Grid Operations with Distributionally Robust, Physics Informed, Safe AI – Led by Oklahoma State University
- AI-powered frameworks to enhance grid resilience under high-dimensional uncertainties – Led by Arizona State University
Recentering Microelectronics in America
- POLARIS: Phase-logic Optimization via Learning and AI for Reduced-temperature Integrated Systems – Led by University of Southern California
Achieving AI-Driven Autonomous Laboratories
- Hierarchical Multi-Modal AI for the Discovery of Regioselective Molecular Catalysts – Led by UC Berkeley
Designing Materials with Predictable Functionality
- AI-Driven Inverse Design of Patchy DNA Origami for Assembly of Programmable Superlattices – Led by Duke University
Unifying Physics from Quarks to the Cosmos
- Enabling Rare Event Discovery with Surrogate Modeling and Simulation AI Agents – Led by University of North Carolina at Chapel Hill
Cybersecurity for AI-driven Science Workflows
- PARSE: Physics-Informed Adversarial Robustness for Scientific AI-Enabled Workflows – Led by University of Arizona