Google commits $40M to the Genesis Mission | Google Cloud Blog

Google is committing $40 million in AI tokens and cloud credits to support the DOE's Genesis Mission and accelerate groundbreaking scientific discovery. Public Sector Accelerating the frontiers of scientific discovery: Google’s $40M commitment to the Genesis Mission July 22, 2026 Pushmeet Kohli VP for Science and Strategic Initiatives, Google DeepMind, and Chief Scientist, Google Cloud Karthik Narain Chief Product and Business Officer, Google Cloud Google AI for Public Sector Empowering an Adaptive, Responsible, Secure, and Intelligent way forward Scientists today face challenges of extraordinary scale and complexity From shaping and simulating the intricate dynamics of fusion plasma, to exploring the vast search space of new materials, to making sense of the exabytes of data pouring out of the world's most advanced experimental facilities

Details

The demands on modern research are unprecedented Frontier AI can help address these challenges, while accelerating groundbreaking scientific discoveries In December, we shared our commitment to the White House's Genesis Mission — the national effort to harness AI and double the pace of American scientific discovery within a decade Since then, Google DeepMind GDM announced an early access program that provides AI for science tools to all 17 Department of Energy DOE National Laboratories, and Google Public Sector shared how Gemini for Government could serve as an AI backbone for the DOE

Context

Today, at the DOE Genesis Mission Summit 2026, we are expanding this by committing $40 million of AI tokens and cloud credits for researchers in support of the Genesis Mission Frontier AI tools for scientific discovery Under this expanded commitment, we will first provide DOE’s Genesis Mission awardees in-kind access to GDM’s frontier AI for science portfolio, including: AlphaEvolve — a Gemini-powered coding and discovery agent, for designing advanced algorithms

Impact

AlphaFold 3 — a model for predicting the structure and interactions of proteins and other biomolecules AlphaGenome — a tool for understanding how variation in DNA, including the non-coding genome, shapes biology and disease WeatherNext — a state-of-the-art family of AI weather forecasting models for mapping weather conditions AlphaEarth Foundations — a foundational AI model for mapping and understanding our planet in unprecedented detail

What's Next

Second, we will provide Gemini for Government seats and tokens for one year to tens of thousands of users across the DOE National Laboratories’ operations, research, and management teams This secure platform supports the full breadth of work from the research bench to the administration of specialized user facilities serving the entire scientific community, providing a single secure foundation that the DOE mission can depend on

Background

AI for science tools in action across the laboratory ecosystem While we have a lot of work still to do, the practical impact of the Genesis Mission is already coming to life across the laboratory ecosystem At Pacific Northwest National Laboratory PNNL, senior scientist Dr Henry Kvinge is using AlphaEvolve to map out massive mathematical systems that are far too complex for humans to explore by hand

Analysis

The AI uncovers hidden connections automatically, fast-tracking discoveries that would normally take researchers years to find “Modern math relies on abstraction, but combinatorics offers concrete models that make complex geometry and algebra easier to grasp We’ve found that systems like AlphaEvolve are perfect for this search,” said Dr “By leveraging the broad mathematical knowledge of LLMs, we can automate the exploration of countless angles

More

We’re still experimenting, but the discoveries are already shaping our future research.” At the National Laboratory of the Rockies NLR, researchers are utilizing Gemini to fundamentally change how they interact with physical laboratory hardware Spurgeon, a senior materials data scientist at NLR, leads a pioneering program in autonomous materials discovery