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Creators/Authors contains: "Gomes, Gabe"

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  1. ; ; ; ; ; (, Nature Computational Science)
    Large language models (LLMs) offer opportunities for advancing chemical research, including planning, optimization, data analysis, automation and knowledge management. Deploying LLMs in active environments, where they interact with tools and data, can greatly enhance their capabilities. However, challenges remain in evaluating their performance and addressing ethical issues such as reproducibility, data privacy and bias. Here we discuss ongoing and potential integrations of LLMs in chemical research, highlighting existing challenges to guide the effective use of LLMs as active scientific partners. 
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    Free, publicly-accessible full text available June 24, 2026
  2. ; ; ; ; (, Nature Machine Intelligence)
    Free, publicly-accessible full text available May 1, 2026
  3. ; ; (, Journal of the American Chemical Society)
    Free, publicly-accessible full text available February 19, 2026
  4. ; ; ; (, Nature)
    Abstract Transformer-based large language models are making significant strides in various fields, such as natural language processing1–5, biology6,7, chemistry8–10and 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. 
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