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Autonomous chemical research with large language models

Author

Listed:
  • Daniil A. Boiko

    (Carnegie Mellon University)

  • Robert MacKnight

    (Carnegie Mellon University)

  • Ben Kline

    (Emerald Cloud Lab)

  • Gabe Gomes

    (Carnegie Mellon University
    Carnegie Mellon University
    Carnegie Mellon University)

Abstract

Transformer-based large language models are making significant strides in various fields, such as natural language processing1–5, biology6,7, chemistry8–10 and computer programming11,12. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution. Our findings demonstrate the versatility, efficacy and explainability of artificial intelligence systems like Coscientist in advancing research.

Suggested Citation

  • Daniil A. Boiko & Robert MacKnight & Ben Kline & Gabe Gomes, 2023. "Autonomous chemical research with large language models," Nature, Nature, vol. 624(7992), pages 570-578, December.
    • Handle: RePEc:nat:nature:v:624:y:2023:i:7992:d:10.1038_s41586-023-06792-0
    DOI: 10.1038/s41586-023-06792-0
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    Cited by:

    1. Haotian Chen & Xinjie Shen & Zeqi Ye & Wenjun Feng & Haoxue Wang & Xiao Yang & Xu Yang & Weiqing Liu & Jiang Bian, 2024. "Towards Data-Centric Automatic R&D," Papers 2404.11276, arXiv.org, revised Jul 2024.
    2. Xiaoning Qi & Lianhe Zhao & Chenyu Tian & Yueyue Li & Zhen-Lin Chen & Peipei Huo & Runsheng Chen & Xiaodong Liu & Baoping Wan & Shengyong Yang & Yi Zhao, 2024. "Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    3. Mateusz Płoszaj-Mazurek & Elżbieta Ryńska, 2024. "Artificial Intelligence and Digital Tools for Assisting Low-Carbon Architectural Design: Merging the Use of Machine Learning, Large Language Models, and Building Information Modeling for Life Cycle As," Energies, MDPI, vol. 17(12), pages 1-21, June.

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