Autonomous chemical research with large language models
Автономные химические исследования с использованием больших языковых моделей
2023-12-20
SCID: 54.1/r4j4yxtm
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CoscientistGPT-4autonomous experimental designexperimental automationpalladium-catalysed cross-couplings reaction optimization
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Abstract (AI)
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.
Key Findings
1
Coscientist accelerated research across six diverse tasks demonstrating versatility and efficacy in chemical research.
2
Coscientist exhibits advanced (semi-)autonomous experimental design and execution with explainable decision-making.
3
Coscientist integrates LLMs with tools including internet/documentation search, code execution, and experimental automation.
4
Coscientist successfully optimized a palladium-catalysed cross-coupling reaction, showing practical experimental success.
5
Coscientist, an AI system driven by GPT-4, autonomously designs, plans, and performs complex chemical experiments.
Research Object
Coscientist, an AI system driven by GPT-4 that autonomously designs, plans, and performs complex chemical experiments
Research Subject
The system's capabilities and performance in autonomous (and semi-autonomous) experimental design, planning, execution, and reaction optimization (including palladium-catalysed cross-couplings) enabled by LLMs and tool integration
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2023-12-20
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