Genesis Mission: Bay Area Labs Join a National AI Push for Faster Science
Three Bay Area national laboratories - Lawrence Livermore, Lawrence Berkeley, and SLAC National Accelerator Laboratory - are joining the Genesis Mission, a U.S. Department of Energy initiative to accelerate discovery with artificial intelligence. The program was launched by executive order signed by President Donald Trump and brings together 17 national labs under one coordinated effort.
The goal is clear: build an integrated AI platform that learns from massive scientific datasets, tests hypotheses, automates workflows, and delivers results fast enough to give the U.S. a decisive advantage in science and national security.
What the Program Will Build
DOE will assemble a unified AI platform that draws on datasets from every participating lab. Think particle physics archives, X-ray diffraction patterns, genomic data, materials simulations, geospatial imagery, and more - all fueling models tuned for scientific workloads.
This is not about chat interfaces. Lawrence Berkeley National Laboratory is focused on models that handle scientific images, molecular structures, and geographical data at scale, trained on top-tier compute resources.
Why It Matters for Researchers
- Faster iteration: AI-assisted hypothesis testing, experiment planning, and analysis loops.
- Automation: Data prep, instrument ops, and post-processing integrated into reproducible workflows.
- Cross-domain transfer: Methods that work across physics, chemistry, biology, and climate datasets.
- Scale: Shared access to national computing plus standardized data interfaces.
- Benchmarks: Common evaluation across labs to compare models and methods credibly.
Bay Area Roles and Strengths
SLAC National Accelerator Laboratory (Menlo Park) brings data from the smallest to the grandest scales - from electrons to cosmic surveys. "We're really excited," said Chris Tassone, associate lab director of energy sciences at SLAC. "This is building on work that we and the other 17 national labs have been doing for the last decade in understanding how we use AI to enable and improve science and technology research."
On expected impact, Tassone added: "By developing artificial intelligence approaches that are going to impact both of those data streams, it means we're going to be impacting science everywhere. That broad impact is what's so exciting about Genesis Mission. It's not just what SLAC is doing, it's what all 17 national labs can do together."
Lawrence Berkeley National Laboratory will lean on its depth in computing and its multi-domain data. "We've had internal projects to look at how AI can be used to change the way we do all kinds of science," said Jonathan Carter, associate lab director of computing sciences. "Now that we have a national initiative that involves all 17 labs, I think we will make progress much more quickly."
Carter pointed to the lab's data and compute as a foundation: "LBNL has a lot of data from different kinds of scientific domains. It has genomic data. It has X-ray diffraction data, where you can look at the structure of proteins and other biomolecules. As well as data, you need computing capabilities to train the models. And we have significant computing capabilities here."
Lawrence Livermore National Laboratory will guide strategy and platform design through Brian Spears, director of the AI Innovation Incubator and technical director for Genesis Mission. "We're thrilled to see this long-term vision finally come to fruition," Spears said. "We have decades of leadership in supercomputing, scientific code development and using data to drive extremely high-consequence decisions."
On outcomes, Spears was direct: "At the end of the day, our goal is simple: build a system that learns, adapts and delivers results fast enough to give the U.S. a decisive advantage in science and national security."
Practical Considerations for Lab Teams
- Data readiness: Adopt FAIR practices; standardize metadata, provenance, and units. Prioritize high-signal datasets with clear labels or measurable targets.
- Workflow ops: Containerize pipelines (e.g., Apptainer/Singularity), use workflow engines, and log everything for repeatability.
- Model governance: Plan for validation, uncertainty quantification, bias checks, and domain-specific evaluation metrics.
- Compute strategy: Align HPC queue policies with training schedules; use mixed precision, distributed training, and profiling to control costs.
- Security and policy: Address export controls, classified boundaries, and data-sharing agreements early.
- Human-in-the-loop: Define review points where domain experts audit results before decisions or publications.
What to Watch Next
Expect early platform prototypes that unify multimodal scientific data and benchmark suites spanning physics, materials, biosciences, and climate. Cross-lab standards, shared APIs, and reference workflows will matter as much as the models.
With Spears serving as technical director and the DOE coordinating across 17 labs, the near-term focus is aligning datasets, compute, and policy so researchers can start shipping real results faster.
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