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Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis.

Amirreza Mottafegh1, Gwang-Noh Ahn1, Dong-Pyo Kim1

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Meta optimization (MO) efficiently benchmarks surrogate models in real-time for microflow organic synthesis. This approach outperforms traditional Bayesian optimization (BO) in optimizing reaction conditions.

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Area of Science:

  • Organic Synthesis
  • Chemical Engineering
  • Machine Learning

Background:

  • Optimizing microflow-based organic synthesis is complex.
  • Bayesian optimization (BO) guides flow reactor conditions but model selection is inefficient.

Purpose of the Study:

  • To introduce meta optimization (MO) for real-time surrogate model benchmarking.
  • To enable efficient optimization of reaction conditions in microflow synthesis.

Main Methods:

  • MO benchmarks multiple surrogate models in real-time without pre-work.
  • Evaluates expected values from regressors to build surrogate models.

Main Results:

  • MO consistently outperformed various Bayesian optimization (BO) methods across four flow synthesis datasets.
  • Conventional BOs showed variable performance depending on the surrogate model (e.g., Gaussian process, random forest).

Conclusions:

  • Meta optimization (MO) provides a superior and efficient method for optimizing microflow reaction conditions.
  • Real-time benchmarking is crucial for selecting optimal surrogate models in BO.