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A chemical reaction is a process by which the bonds in the atoms of substances are rearranged to generate new substances. Matter cannot be created or destroyed in a chemical reaction—the same type and number of atoms that make up the reactants are still present in the products. Merely, the rearrangement of chemical bonds produces new compounds.
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Bayesian optimization for chemical reactions.

Stefan Desimpel1, Matthieu Dorbec2, Kevin M Van Geem3

  • 1SynBioC Research Group, Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium. chris.stevens@ugent.be.

Chemical Society Reviews
|February 10, 2026
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Summary
This summary is machine-generated.

Bayesian optimization (BO) accelerates complex chemical reaction optimization using data efficiently. This review guides chemists on applying BO fundamentals, practical aspects, and diverse applications for data-driven chemical strategies.

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Area of Science:

  • Chemistry
  • Chemical Engineering
  • Data Science

Background:

  • Chemical reaction optimization is complex and data-intensive.
  • Bayesian optimization (BO) offers a data-efficient approach.
  • Large, mixed-variable parameter spaces pose challenges.

Purpose of the Study:

  • To provide an accessible introduction to Bayesian optimization for chemists.
  • To outline the fundamentals, practical considerations, and applications of BO in chemistry.
  • To discuss emerging directions for accelerating chemical optimization.

Main Methods:

  • Review of surrogate models and acquisition functions in BO.
  • Discussion of kernel design and categorical variable representation.
  • Survey of BO applications across various experimental scales.

Main Results:

  • BO balances exploration and exploitation for efficient optimization.
  • Practical strategies for multi-objective and batch optimization are presented.
  • Applications span high-throughput screening to automated flow reactors.

Conclusions:

  • Bayesian optimization is a powerful tool for data-efficient chemical process optimization.
  • Emerging trends like transfer learning can further accelerate optimization.
  • BO enables more generalizable, data-driven strategies in chemistry.