The Genesis Mission: Accelerating Scientific Discovery With AI
The Department of Energy's Genesis Mission is a national push to advance scientific discovery, engineering, and innovation using artificial intelligence and advanced computing. It connects supercomputers, data resources, AI and quantum systems, and scientific instruments into a secure discovery platform focused on priority science and technology challenges.
As a coordinated effort across government, industry, and academia, the program strengthens the nation's technological capability, global competitiveness, energy security, and national defense. The scope spans the Energy Department's 17 national laboratories and draws on the expertise of roughly 40,000 scientists, engineers, and technical staff.
What the Genesis Mission Is For
- Accelerate hypothesis generation, simulation, and validation across domains like materials, biology, chemistry, climate, and energy systems.
- Integrate instruments, data streams, and models to shorten experiment cycles and scale autonomous discovery.
- Advance secure AI development and data management for sensitive missions.
- Build shared infrastructure that enables reproducible, auditable, and collaborative science.
Who's Involved
The initiative unites all 17 DOE national labs with industry and academic partners under a secure, integrated platform. The goal: move from siloed projects to a connected system where datasets, models, and instruments can work together across sites.
An initial 24 organizations signed collaboration agreements with the department to develop scalable AI capabilities and shared R&D infrastructure. This public-private model concentrates compute, data, and know-how where it matters most.
Key Roles Across the DOE Complex
PNNL: Building AI-driven capabilities for autonomous discovery in chemistry, materials, and biology; speeding environmental permitting; and improving grid security through advanced modeling.
NNSA: Leading classified AI development, data management, and advanced model capabilities for mission-critical applications.
ORNL: Advancing two new computing systems-Discovery and Lux-that accelerate AI-driven research and help stand up the American Science Cloud.
Berkeley Lab's Projects
With deep roots in computational science, mathematics, and data analysis, Berkeley Lab is a core contributor. It leads efforts including:
- MOAT (Multi-Office particle Accelerator Team) for accelerator science.
- SYNAPS-I (Synergistic Neutron and Photon Autonomous Science - Imaging) for autonomous imaging workflows.
- OPAL (Orchestrated Platform for Autonomous Laboratories) to accelerate AI-driven biodesign.
Private-Sector Collaborations
Anthropic: Multi-year work across energy systems, bio/life sciences, and research productivity. Anthropic provides tools and expertise to link models with scientific data, instruments, and workflows, building on prior DOE collaborations.
Oracle: A non-binding agreement to support current and future AI and advanced computing initiatives, including the Genesis Mission. Focus areas include domestic compute and data capabilities, responsible AI practices, and an integrated platform spanning facilities and datasets.
NVIDIA: A memorandum of understanding extends collaboration on open AI science models, AI-driven manufacturing and supply chains, nuclear energy, quantum computing, robotics, and materials and biological sciences.
What This Enables for Research Teams
- Closed-loop science: Couple instruments, simulations, and AI-driven decisioning to iterate experiments in hours, not months.
- Multimodal data fusion: Combine imaging, spectroscopy, and sensor data to improve model fidelity and uncertainty estimates.
- Scaled workflows: Move from local scripts to portable, auditable pipelines that run across facilities.
- Faster permitting and grid studies: Use surrogate models and scenario analysis to stress-test policies and infrastructure.
- Secure collaboration: Apply access controls and governance to share what you can, protect what you must.
How to Prepare Your Lab
- Data readiness: Standardize schemas, add rich metadata, and document lineage. Prioritize datasets with high scientific yield.
- Workflow portability: Containerize pipelines and define them as code. Target reproducibility from day one.
- Model governance: Track datasets, prompts, model versions, and evaluation results. Log assumptions and risks.
- Validation and uncertainty: Establish benchmarking, UQ, and red-teaming for safety and reliability.
- Compute strategy: Plan for hybrid use of facility HPC, cloud, and on-prem clusters with clear cost and scheduling policies.
- People and skills: Cross-train domain scientists, data engineers, and research software engineers on AI workflows and facility tooling.
Where to Learn More
For agency updates and program context, start with the U.S. Department of Energy.
If your team is building AI capability for scientific work, explore role-specific upskilling options: AI courses by job.
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