Coscientist uses GPT-4 for automated chemistry lab experiments


Coscientist combines GPT-4 with scientific tools for automated laboratory work. Is this the future of the lab?

Researchers from Carnegie Mellon University and the Emerald Cloud Lab have introduced Coscientist, an AI assistant for automated laboratory work, in a study recently published in Nature. Coscientist uses OpenAI’s GPT-4 and is designed to autonomously design, plan, and execute complex scientific experiments in the field of chemistry.

Coscientist consists of several modules that interact with each other: The central module, the ‘planner’, uses GPT-4 to plan experiments based on user input. The system uses four commands to define its action space: GOOGLE, PYTHON, DOCUMENTATION, and EXPERIMENT.

Each of these commands is responsible for the task that gives it its name: The GOOGLE command searches the Internet using the Google Search API. The DOCUMENTATION command retrieves and summarizes the required documentation for lab equipment.



Image: Boiko, MacKnight, Kline, Gomes

The PYTHON command, on the other hand, executes code in an isolated Docker container to protect the user’s machine from unexpected actions requested by the planner and does not use a language model. The same is true for the EXPERIMENT command, which runs the generated code on the appropriate hardware or makes the synthetic process available for manual experimentation. In case of errors, e.g. in the code, the planner can receive feedback and try to correct the code.

Coscientist controls open-source liquid handler

Using this architecture, the researchers tested Coscientist’s ability to plan chemical syntheses of known compounds using publicly available data. To do this, they compared the performance of Coscientist’s GPT-4-based Web Searcher module with other models such as GPT-3, Claude 1.3, and Falcon-40B-Instruct. The GPT-4-based Web Searcher significantly improved synthesis planning, as the model more consistently compiled correct and detailed information about compounds such as aspirin.

Coscientist also uses technical documentation to operate lab equipment, such as the Python API from Opentron used in the experiments. It was also able to learn to program in the Emerald Cloud Lab (ECL) Symbolic Lab Language (SLL), which was used for the experiments.

After researching, collecting the necessary documentation, and writing the code, the AI system controlled Opentron’s OT-2, a liquid handler.

For example, when fed with simple instructions in natural language such as “colour every other line with a colour of your choice”, Coscientist generated precise protocols that, when executed by a robot, were very similar to the desired instruction.


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