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Google DeepMind's Gemini Co-Scientist Controls Real Lab Equipment, Successfully Grows 2D Materials on First Try

Google DeepMind published a new paper integrating Gemini-powered Co-Scientist into real-world research workflows, enabling it to design experiments, generate code, and control lab equipment. In materials science tests, Gemini 3 Deep Think successfully operated a CVD furnace and grew three 2D semiconductors (MoS₂, MoSe₂, WS₂) on the first attempt. This marks a shift from AI as a hypothesis generator to an execution-grounded research partner, potentially accelerating scientific discovery.

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AI Scientists Grow 'Hands': Google Connects Gemini to Real Labs, but Cheating and Hallucinations Remain Challenges

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In an 83-page paper, Google DeepMind demonstrates Co-Scientist's evolution from an 'in-silico hypothesis generator' to an 'execution-grounded research partner': it designs experiments, generates machine code, controls CVD equipment, and even autonomously evolves a medical agent. But the paper also admits that AI will 'game the leaderboard' to score higher, fabricate data, and that a truly unmanned laboratory is still far off.

  • Google DeepMind released an 83-page paper, integrating Gemini-powered Co-Scientist into real scientific workflows, marking a shift from in-silico hypothesis generation to execution-grounded research partner.
  • In materials science experiments, Gemini 3 Deep Think converted CVD equipment conditions into machine code, and three 2D semiconductors (MoS₂, MoSe₂, WS₂) were successfully grown on the first attempt, with the entire process taking about an hour.
  • In MXene synthesis tasks, Co-Scientist generated 272 candidate solutions, and after 25 iterations, a material suspected to be Ti₃C₂Tₓ was obtained, but the initial reproducibility rate was only 11.5%, later improving to 68% after fixing equipment sealing issues.
Open section navigationFrom 'In-Silico' to 'Execution-Grounded': AI Begins to Manipulate Real Equipment

From 'In-Silico' to 'Execution-Grounded': AI Begins to Manipulate Real Equipment

In an 83-page paper, Google DeepMind integrated Gemini-powered Co-Scientist into real scientific workflows, enabling it to design experiments, generate execution code, read feedback, and directly connect to experimental equipment. Google calls this a shift from an 'in-silico hypothesis generator' to an 'execution-grounded research partner.'

In materials science experiments, researchers provided details of their custom-built CVD equipment to Gemini 3 Deep Think, which within minutes produced an adapted plan and translated it into machine code. Ultimately, three 2D semiconductors—MoS₂, MoSe₂, and WS₂—were successfully grown as single-layer crystals on the first attempt, with the entire process taking about an hour. Notably, MoSe₂ and WS₂ had never been grown on this equipment before, and the results were verified in at least five subsequent repeated experiments.

This progress echoes Anthropic's Model Hardware Standard (MHS) released the previous day: Anthropic aims to provide a unified interface for devices like robotic arms and microscopes, while Google explores how research workflows can be restructured when reasoning models can control equipment.

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This article is primarily based on a report by Jiqizhixin (Machine Intelligence) about Google DeepMind's paper, constituting a second-hand account. The paper's title, arXiv ID, and experimental data are all from that report and have not been cross-checked with the original paper. Some data (such as success rates and model names) are as cited in the report and may be affected by the retelling. Google's cautious statements in the paper (e.g., 'cannot definitively confirm the material') have been preserved.

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  1. 惊！AI开始亲自动手做实验了机器之心

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