Gemini for Science: AI experiments and tools for a new era of discovery

Gemini for Science: AI experiments and tools for a new era of discovery For centuries, the scientific method has been the greatest engine of human progress. At Google, our mission is deeply rooted in building tools to accelerate it. We believe that a new era of discovery won’t come from narrow, specialized models, but general agents that empower researchers across every scientific field. That’s why we are introducing Gemini for Science, a collection of science tools and experiments designed to expand the scale and precision of scientific exploration. A force multiplier for human ingenuity Today science faces a paradox: our collective knowledge is growing so fast that it’s becoming harder for individual scientists to see the full picture. Scientific breakthroughs often rely upon making creative connections between data, but the time required to do this manually can take weeks or even months. AI can help eliminate this bottleneck and serve as a force multiplier for scientific work by handling complex tasks. This allows researchers to focus on identifying and tackling the most impactful scientific problems and directions that would drive progress. Gemini for Science experimental tools on Google Labs include three primary prototypes designed to handle such tasks. - Hypothesis Generation, built with Co-Scientist: Ideation is the heartbeat of science, but no human can synthesise the millions of papers published annually. Hypothesis Generation bridges this gap by simulating the scientific method: it collaborates with researchers to define a research challenge, then uses a multi-agent “idea tournament” to generate, debate and evaluate hypotheses. To ensure absolute rigor, claims are deeply verified and supported by clickable citations. - Computational Discovery, built with AlphaEvolve and ERA (Empirical Research Assistance): Scientific progress is often limited by the number of hypotheses we can realistically test with computational experiments. Computational Discovery, an agentic research engine, is a prototype that solves this by generating and scoring thousands of code variations in parallel. This allows scientists to test novel modeling approaches — for complex fields like solar forecasting or epidemiology — that would take months to navigate manually. - Literature Insights, built with Google NotebookLM: Understanding scientific literature is a core part of all research journeys. Literature Insights searches scientific literature and structures results into tables with custom, searchable attributes for side-by-side analysis. Researchers can use chat to uncover nuances grounded in their curated corpus, and create high-fidelity artifacts such as reports, slide decks, infographics and audio and video overviews. With the power of NotebookLM, Literature insights helps synthesize findings across papers, identify research gaps and uncover areas of opportunity. Starting today, we’ll begin gradually opening access to these experiments. Visit labs.google/science to register your interest. Beyond the individual experiments, we’re also bringing these advanced AI capabilities to enterprise organizations through Google Cloud. Our enterprise-grade solutions for scientific and industrial R&D are already being used by a range of partners in private preview to drive real-world impact. Companies like BASF are using AlphaEvolve to optimize their supply chains, and Klarna is leveraging it to enhance their machine learning…