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Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis.

Yehia Amar1, Artur M Schweidtmann2, Paul Deutsch3

  • 1Department of Chemical Engineering and Biotechnology , University of Cambridge , Philippa Fawcett Drive , Cambridge , CB3 0AS , UK .

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This study introduces a hybrid mechanistic-machine learning approach for automated solvent selection in chemical process development. The method optimizes reactions for high conversion and diastereomeric excess, paving the way for robotic laboratories.

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Area of Science:

  • Chemical Engineering
  • Computational Chemistry
  • Process Development

Background:

  • Solvent selection is a critical bottleneck in optimizing chemical reactions.
  • Developing predictive models for solvent effects requires significant experimental data and expertise.

Purpose of the Study:

  • To develop an automated, hybrid mechanistic-machine learning approach for rational solvent selection.
  • To optimize a Rh(CO)2(acac)/Josiphos catalyzed asymmetric hydrogenation for high conversion and diastereomeric excess.

Main Methods:

  • Calculated molecular and reaction-specific descriptors for 459 solvents.
  • Trained Gaussian process surrogate models on experimental data for conversion and diastereomeric excess.
  • Employed multi-objective optimization and Bayesian optimization for solvent and condition screening.

Main Results:

  • Identified optimal solvents and solvent mixtures for improved reaction outcomes.
  • Achieved a cross-validation correlation coefficient of 0.84 for the conversion prediction model.
  • Demonstrated a genetic programming approach for selecting appropriate machine learning models.

Conclusions:

  • The hybrid approach enables efficient and automated solvent selection in process development.
  • This methodology supports the integration of machine learning into future robotic chemical laboratories.
  • Predictive modeling significantly accelerates the optimization of complex catalytic reactions.