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Autonomous chemical research with large language models

2023/12/20 by Daniil A. Boiko, Robert MacKnight, Ben Kline +1 · 174 citations
Engineering · Materials Science · #Ferroelectric and Negative Capacitance Devices #Innovative Microfluidic and Catalytic Techniques Innovation #Machine Learning in Materials Science

paper · pdf · doi:10.1038/s41586-023-06792-0

openalex publication_date 2023/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

Abstract Transformer-based large language models are making significant strides in various fields, such as natural language processing 1–5 , biology 6,7 , chemistry 8–10 and computer programming 11,12 . 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.

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