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Cost-informed Bayesian reaction optimization.

Alexandre A Schoepfer1,2,3, Jan Weinreich1,3, Ruben Laplaza1,3

  • 1Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland clemence.corminboeuf@epfl.ch.

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This summary is machine-generated.

Cost-informed Bayesian optimization (CIBO) prioritizes cost-effective chemical experiments. This approach reduces chemical reaction optimization costs significantly by considering reagent expenses.

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Area of Science:

  • Chemical Research
  • Computational Chemistry
  • Process Optimization

Background:

  • Bayesian optimization (BO) is popular for chemical research but ignores experimentation costs.
  • Standard BO does not differentiate between readily available and costly reagents.

Purpose of the Study:

  • Introduce cost-informed Bayesian optimization (CIBO) for rational chemical experiment planning.
  • Prioritize cost-effective experiments in chemical research.

Main Methods:

  • Developed CIBO algorithm to track reagent availability and dynamically update costs.
  • CIBO uses anticipated performance improvement versus reagent cost for experiment selection.

Main Results:

  • CIBO reduced the cost of optimizing Pd-catalyzed reactions by up to 90% compared to standard BO.
  • Demonstrated CIBO's effectiveness using literature data.

Conclusions:

  • CIBO offers a cost-effective alternative to standard BO for chemical experimentation.
  • The approach is versatile, accommodating various cost factors like time, environmental, and security concerns.
  • CIBO is applicable to both traditional and automated laboratories for experiment planning.