Complete List of Brookhaven Lab Genesis Mission Phase I Projects
Brookhaven National Laboratory will lead seven Genesis Mission projects and contribute to an additional 29
July 27, 2026
Seven innovative research projects led by the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have been selected by DOE to receive funding through the Genesis Mission Phase I Request for Application. The awards to Brookhaven Lab support groundbreaking artificial intelligence research for microelectronics, nuclear and high energy physics, computational science, and atmospheric science. In addition to the Brookhaven-led projects, the Lab is contributing to 29 additional projects led by other National Laboratories, research universities, and private industry. Below is a complete list of these 36 projects, their lead institutions, and their principal investigators.
Brookhaven National Laboratory
Press release: Brookhaven Lab Awarded 7 Genesis Mission Projects
AI-Driven, Self-Learning Digital Twins for Robust Operation of Particle Accelerators
Kevin Brown
Cross-Domain Scientific Reasoning through Composable Foundation Models
David Park
An Automated, Multimodal-AI-Enabled Cloud Chamber for Constraining Cloud Microphysical Processes in Earth System Models
Arthur Sedlacek
MARS: scaling Multi-Agent Reinforcement learning for Scientific hypothesis generation
Yihui Ren
Deployable Cavity Coupled Cold Atom Quantum Sensing Platform Driven by Agentic AI
Gabriella Carini
From Materials to Circuits: An AI-Native EDA Framework for Physics-Based Microelectronics Co-Design
Soumayajit Mandal
Transforming Computing Cyber infrastructure for Collider Experiments to AI Based Computing and AI-ready Data
Alexei Klimentov
Stony Brook University
Foundation Models for Transferable Particle Tracking in Nuclear Physics
Jan Bernauer
AI-Driven Autonomous and Self-Healing Readout Electronics for Radiation-Tolerant HEP Detectors
Emre Salman
Columbia University
Press release: Columbia University Projects Receive Genesis Mission Funding
AI-accelerated sampling for critical slowing down in lattice QCD
Norman Christ
AI-Enabled Physical Operating System for Bio-programmable Matter
Kyle Bishop
Duke University
Press release: Four Duke Teams Selected for New Federal AI Research Program
Neuromorphic Circuit Primitives for Robotic Embodied Physical AI
Yiran Chen
Louisiana State University
Press release: LSU Wins Two Highly Competitive Genesis Mission Awards
AI for NSLS-II/IIU and CAMD: from early design to high-performance operations
Phillip Sprunger
Princeton Plasma Physics Laboratory
AI-4-Gyrotron: accelerating ECH technology delivery for nuclear fusion
Syun'ichi Shiraiwa
University of Oregon
Press release: UO physicist earns Genesis Mission award
MANGO - Monte Carlo Acceleration via Normalizing Flows Using GPU Optimization
Stephanie Majewski
New Mexico State University
Press release: NMSU-led team among those selected for $800 million DOE Genesis Mission
AI-Accelerated Non-Abelian Gauge Dynamics on Quantum Hardware
Raza Sufian
Michigan Technological University
AI-accelerated exploration of droplet collision-coalescence using observation-constrained, multiscale modeling of a turbulent-convection cloud chamber
Raymond Shaw
University of Kentucky
Press release: 6 UK researchers selected for historic DOE ‘Genesis Mission’
A Physically-Informed Neural Network for Power-grid Resilience to Extreme Wind Events
Sean Bailey
Pennsylvania State University
Press release: Penn State scientists to lead three Genesis Mission projects
Transient Kinetics and Spectroscopy for Agentic Digital Twins to Upgrade Domestic Alkane Feedstocks into Value-Added Chemicals
Michael Janik
University at Buffalo
Press release: UB scientists awarded Genesis Mission grants for AI-focused research
CLEAR-AI: Closed-Loop, Efficient, Adaptive, and Robust AI for Agentic Chemical Manufacturing
Jiayu Peng
William & Mary
Mixture-of-Experts Foundation Models for Scalable Holistic Reconstruction, Modeling, and Interpretation of Particle Interactions
Cristiano Fanelli
Johns Hopkins University
Press release: Hopkins-led teams selected for inaugural Department of Energy Genesis Mission awards
Neuro-Symbolic Synthesis of Verified Computational Physics Code for Scientific Discovery
Ziyang Li
Texas State University
OPTIX: A Multi-Modal Foundation Model for Performance-Aware HPC Code Intelligence
Tanzima Islam
University of Nebraska-Lincoln
Press release: Husker-led project receives Genesis Mission funding to advance AI, 6G
SwarmSlicer: AI-Driven Design of Sensor-Network-GPU Slicing in 6G Wireless Networks for Distributed Autonomous Cyber-physical Testbeds
Mehmet Vuran
Cornell University
Press release: DOE Genesis Mission awards will advance AI-driven science
SPECTRA: SPECTRAL PREDICTION AND ELECTRONIC CHARACTERIZATION THROUGH RAPID AI
Kyle Shen
Great Sky Inc.
Press release: Great Sky Selected for DOE’s Genesis Mission
Highly connected superconducting optoelectronic neuromorphic systems for scalable AI
Jeffrey Shainline
Lawrence Livermore National Laboratory
Press release: LLNL selected to lead 10 projects under DOE’s Genesis Mission
Multimodal Enhancements to the Foundation Model for Nuclear and Particle Physics (FM4NPP)
Ron Soltz
BIND: Biophysics-Informed Learning of Coordination for Metalloprotein Design
Yongqin Jiao
Massachusetts Institute of Technology
Press release: MIT projects selected for funding under US Department of Energy’s Genesis Mission
Multi-modal and multi-facility application of the FM4NPP foundation model: silicon trackers and electron colliders
Gunther Roland
AI-Driven Quantum Sensing for Precision Tests of Fundamental Physics
Ronald Fernando Garcia Ruiz
National Laboratory of the Rockies
Turbulence-Microphysics-Land Nexus (TML-Nexus): Unlocking Predictive Power for the Coupled Water Cycle using Weather Foundation Models
Sengupta, Manajit
Florida State University
AI-Driven Decoding for Next Generation Topological Quantum Codes
Nicholas Bonesteel
St. Joseph's University
Genesis Mission: Accelerating ENSDF Nuclear Data Evaluation with Physics-Informed AI
Victoria Hong
University of Arkansas
An Autonomous Framework for Adversarial Robustness and Resilience of AI Models in Power Grids
Qinghua Li
Baruch College
Characterizing Jet Modification in the Quark-Gluon Plasma Using Unsupervised, Cycle-Consistent Generative Learning
Stefan Bathe
George Mason University
An End-to-End AI Framework for Performance Prediction and Optimization in HPC Applications
Keren Zhou
Brookhaven National Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit science.energy.gov.
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