Google commits $40M to the Genesis Mission
Google DeepMind commits $40 million in AI tokens and cloud credits to support the U.S. Department of Energy’s Genesis Mission, aiming to double the pace of scientific discovery through expanded access to frontier AI tools for national laboratories.
Google DeepMind announced a $40 million commitment of AI tokens and cloud credits to advance the Genesis Mission, a White House initiative to accelerate American scientific discovery using AI. The funding will provide in-kind access to Google DeepMind’s AI for science portfolio, including tools like AlphaEvolve, to all 17 Department of Energy (DOE) National Laboratories. Additionally, Gemini for Government seats and tokens will be made available to tens of thousands of users across DOE operations, research, and management teams for one year.
The expanded commitment builds on earlier efforts, such as an early access program launched in December that provided AI tools to DOE labs. The initiative aims to address challenges in fields like fusion plasma simulation, materials discovery, and data analysis by leveraging advanced AI capabilities. A secure platform will support research workflows from laboratory operations to administrative functions, ensuring a unified foundation for DOE’s scientific mission.
Researchers at Pacific Northwest National Laboratory (PNNL) are already using AlphaEvolve to automate the exploration of complex mathematical systems, reducing discovery timelines from years to months. Dr. Henry Kvinge, a senior scientist at PNNL, noted that the AI uncovers hidden connections in combinatorics, making abstract mathematical models more accessible. The tool is enabling researchers to focus on higher-level analysis while the AI handles labor-intensive exploration.
At the National Laboratory of the Rockies (NLR), researchers are deploying Gemini to automate materials discovery workflows. Dr. Steven R. Spurgeon reported that Gemini reduced microscope calibration time from over 90 minutes to 13 minutes and cut manual focusing steps from 50 to two, enabling autonomous experimentation. The AI-driven system allows scientists to explore material design spaces previously inaccessible through manual methods, accelerating breakthroughs in critical research areas.