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Selecting initial samples for Bayesian optimization (BO) is crucial. A new clustering-based method improves BO efficiency for chemical experiments, reducing the number of required experiments by up to 5%.

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Area of Science:

  • Computational Chemistry
  • Machine Learning
  • Chemical Engineering

Background:

  • Gaussian process regression models in Bayesian optimization (BO) require appropriate initial samples for efficient search.
  • D-optimality is a common method for selecting initial samples, but it struggles with highly correlated molecular descriptors common in chemical experiments.
  • Compounds with similar structures form clusters in chemical space, necessitating sample selection strategies that account for this clustering.

Purpose of the Study:

  • To develop a novel initial sample selection method for Bayesian optimization tailored to chemical experimental designs.
  • To address the limitations of D-optimality when dealing with correlated molecular descriptors and clustered chemical spaces.
  • To improve the efficiency and performance of Bayesian optimization in optimizing chemical reactions.

Main Methods:

  • Proposed a new initial sample selection method based on clustering chemical structures.
  • Applied the clustering-based method to optimize coupling reaction conditions using Bayesian optimization.
  • Compared the performance of the proposed method against random sampling and D-optimality-based sampling.

Main Results:

  • The proposed clustering-based method achieved optimal solutions with up to 5% fewer experiments compared to random sampling.
  • The method demonstrated superior performance over D-optimality in scenarios with highly correlated molecular descriptors.
  • Initial samples selected uniformly from clusters provided maximum information for building the regression model.

Conclusions:

  • The developed clustering-based initial sample selection method enhances the search performance of Bayesian optimization for chemical experiments.
  • This approach offers a significant improvement over traditional methods like D-optimality for optimizing reactions with complex chemical spaces.
  • The method's effectiveness can be further leveraged in various scientific and technological fields by incorporating domain knowledge for appropriate cluster formation.