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

Gemini for Science：开启科学发现新时代的 AI 实验与工具

Explore the future of discovery with new Science Skills in Google Antigravity and three new experimental tools on Google Labs. These tools are designed to help accelerate core steps of the scientific method, built with Co-Scientist, Alpha Evolve, Empirical Research Assistance and NotebookLM. 通过 Google Antigravity 中的全新“科学技能”（Science Skills）以及 Google Labs 上的三款实验性工具，探索发现的未来。这些工具基于 Co-Scientist、Alpha Evolve、经验研究辅助（ERA）和 NotebookLM 构建，旨在帮助加速科学方法的核心步骤。

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. 几个世纪以来，科学方法一直是人类进步的最伟大引擎。在 Google，我们的使命深深植根于构建加速这一进程的工具。我们相信，发现的新时代不会来自狭窄的专用模型，而是来自能够赋能各个科学领域研究人员的通用智能体。因此，我们推出了 Gemini for Science，这是一套旨在扩大科学探索规模和精度的科学工具与实验集合。

A force multiplier for human ingenuity

人类智慧的倍增器

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. 如今，科学面临一个悖论：我们的集体知识增长如此之快，以至于个人科学家越来越难以洞察全貌。科学突破往往依赖于在数据之间建立创造性的联系，但手动完成这一过程可能需要数周甚至数月的时间。AI 可以通过处理复杂任务来消除这一瓶颈，并成为科学工作的倍增器。这使得研究人员能够专注于识别和解决那些最能推动进步的科学问题与方向。

Gemini for Science experimental tools on Google Labs include three primary prototypes designed to handle such tasks. Google Labs 上的 Gemini for Science 实验工具包括三个旨在处理此类任务的主要原型：

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. 假设生成（Hypothesis Generation，基于 Co-Scientist 构建）： 构思是科学的心跳，但没有人能综合每年发表的数百万篇论文。假设生成通过模拟科学方法弥补了这一差距：它与研究人员合作定义研究挑战，然后利用多智能体“创意锦标赛”来生成、辩论和评估假设。为了确保绝对的严谨性，所有主张都经过深度验证，并由可点击的引用支持。
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. 计算发现（Computational Discovery，基于 AlphaEvolve 和 ERA 构建）： 科学进步往往受限于我们通过计算实验所能实际测试的假设数量。计算发现是一个智能体研究引擎，它通过并行生成和评分数千种代码变体来解决这一问题。这使得科学家能够测试新颖的建模方法——例如在太阳能预测或流行病学等复杂领域——而这些方法如果手动操作则需要数月时间。
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. 文献洞察（Literature Insights，基于 Google NotebookLM 构建）： 理解科学文献是所有研究旅程的核心部分。文献洞察可以搜索科学文献，并将结果整理成带有自定义、可搜索属性的表格，以便进行并排分析。研究人员可以使用聊天功能，基于他们整理的语料库挖掘细微差别，并创建高质量的产出物，如报告、幻灯片、信息图表以及音频和视频概述。借助 NotebookLM 的强大功能，文献洞察有助于综合各篇论文的研究发现，识别研究空白并发现机会领域。

Starting today, we’ll begin gradually opening access to these experiments. Visit labs.google/science to register your interest. 从今天开始，我们将逐步开放这些实验的访问权限。请访问 labs.google/science 注册以表达您的兴趣。

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 models. In parallel, organizations like Daiichi Sankyo, Bayer Crop Science and the U.S. National Labs (as part of the U.S. Department of Energy’s Genesis Mission) are using Co-Scientist to accelerate their research and tackle fundamental scientific challenges. These enterprise-grade tools are demonstrating significant value in their current preview phase. We are excited about the breakthroughs our partners are unlocking and look forward to expanding access to more organizations in the coming months. 除了个人实验外，我们还通过 Google Cloud 将这些先进的 AI 能力带给企业组织。我们面向科学和工业研发的企业级解决方案已经在私有预览阶段被多家合作伙伴使用，以推动实际应用。巴斯夫（BASF）等公司正在使用 AlphaEvolve 优化其供应链，Klarna 正在利用它增强其机器学习模型。与此同时，第一三共（Daiichi Sankyo）、拜耳作物科学（Bayer Crop Science）和美国国家实验室（作为美国能源部 Genesis 任务的一部分）等机构正在使用 Co-Scientist 加速其研究并应对基础科学挑战。这些企业级工具在当前的预览阶段展现出了巨大的价值。我们对合作伙伴正在取得的突破感到兴奋，并期待在未来几个月内向更多组织开放访问权限。

Several validation papers have been already published based on these and other tools. The ERA and Co-Scientist research papers are published today in Nature. 基于这些工具及其他工具，多篇验证性论文已经发表。ERA 和 Co-Scientist 的研究论文已于今日在《自然》（Nature）杂志上发表。

A scientific workbench on your desktop

桌面上的科学工作台

As part of Gemini for Science, we are also launching Science Skills, a specialized bundle that integrates insights from over 30 major life science databases and tools including UniProt, AlphaFold Database, AlphaGenome API and InterPro. Using these skills on agentic platforms like Google Antigravity allows researchers to perform complex and often manual workflows like structural bioinformatics and genomic analyses in minutes rather than hours. 作为 Gemini for Science 的一部分，我们还推出了“科学技能”（Science Skills），这是一个集成了来自 30 多个主要生命科学数据库和工具（包括 UniProt、AlphaFold 数据库、AlphaGenome API 和 InterPro）洞察的专业工具包。在 Google Antigravity 等智能体平台上使用这些技能，可以让研究人员在几分钟内（而非几小时）完成结构生物信息学和基因组分析等复杂且通常需要手动操作的工作流程。

Our research teams using Science Skills have already seen this speedup in practice. In early testing, our team used Science Skills to perform a complex analysis that normally takes hours in minutes. This led to novel insights about potential mechanisms for a rare genetic disease caused by mutations in the AK2 gene. To learn more on how to use Science Skills in Google Antigravity visit antigravity.google/use-cases/science. 我们的研究团队在使用“科学技能”时已经见证了这种提速。在早期测试中，我们的团队使用该技能在几分钟内完成了一项通常需要数小时的复杂分析。这带来了关于 AK2 基因突变引起的罕见遗传病潜在机制的新见解。要了解更多关于如何在 Google Antigravity 中使用“科学技能”的信息，请访问 antigravity.google/use-cases/science。

A collaborative effort with the scientific community

与科学界的协作努力

Our commitment to responsibly develop and deploy tools for science begins with the scientific ecosystem. We are collaborating with over 100 institutions — including Stanford University on liver fibrosis, Imperial College London on antimicrobial resistance and a multi-year effort with The Crick Institute — to validate our new systems and tools. To ensure the integrity of AI-generated insights, 我们致力于负责任地开发和部署科学工具，这始于科学生态系统。我们正在与 100 多家机构合作——包括与斯坦福大学合作研究肝纤维化，与伦敦帝国理工学院合作研究抗菌素耐药性，以及与克里克研究所（The Crick Institute）开展多年合作——以验证我们的新系统和工具。为了确保 AI 生成洞察的完整性，