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Bayesian optimization as a valuable tool for sustainable chemical reaction development

Bayesian optimization is a promising approach towards a more environmentally friendly chemical synthesis, in line with the Sustainable Development Goals. It can aid chemists to explore vast chemical spaces and find green reaction conditions with few experiments, decreasing resource consumption and waste generation while reducing discovery timelines and costs.

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Fig. 1: Workflow of Bayesian optimization and its advantages for sustainable chemical development.


  1. Vinuesa, R. et al. The role of artificial intelligence in achieving the Sustainable Development Goals. Nat. Commun. 11, 233 (2020).

    Article  ADS  Google Scholar 

  2. Guo, J., Ranković, B. & Schwaller, P. Bayesian optimization for chemical reactions. Chimia 77, 31–38 (2023).

    Article  Google Scholar 

  3. Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016).

    Article  Google Scholar 

  4. Sheldon, R. A. Metrics of green chemistry and sustainability: past, present, and future. ACS Sustain. Chem. Eng. 6, 32–48 (2018).

    Article  Google Scholar 

  5. Shields, B. J. et al. Bayesian reaction optimization as a tool for chemical synthesis. Nature 590, 89–96 (2021).

    Article  ADS  Google Scholar 

  6. Garrido Torres, J. A. et al. A multi-objective active learning platform and web app for reaction optimization. J. Am. Chem. Soc. 144, 19999–20007 (2022).

    Article  Google Scholar 

  7. Häse, F., Aldeghi, M., Hickman, R. J., Roch, L. M. & Aspuru-Guzik, A. GRYFFIN: an algorithm for Bayesian optimization of categorical variables informed by expert knowledge. Appl. Phys. Rev. 8, 031406 (2021).

    Article  ADS  Google Scholar 

  8. Braconi, E. & Godineau, E. Bayesian optimization as a sustainable strategy for early-stage process development? A case study of Cu-catalyzed C–N coupling of sterically hindered pyrazines. ACS Sustain. Chem. Eng. 11, 10545–10554 (2023).

    Article  Google Scholar 

  9. Clayton, A. D. et al. Bayesian self-optimization for telescoped continuous flow synthesis. Angew. Chem. Int. Ed. 62, e202214511 (2023).

    Article  Google Scholar 

  10. Burger, B. et al. A mobile robotic chemist. Nature 583, 237–241 (2020).

    Article  ADS  Google Scholar 

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The author thanks E. Godineau, O. Lahtigui and S. Bell for valuable discussions and acknowledges Syngenta Crop Protection AG for financial support.

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Correspondence to Elena Braconi.

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The author declares no competing interests.

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Braconi, E. Bayesian optimization as a valuable tool for sustainable chemical reaction development. Nat Rev Methods Primers 3, 74 (2023).

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