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Catalysis meets machine learning: a guide to data-driven discovery and design.

Eleonora Casillo1, Thomas Scattolin2, Steven P Nolan1

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Machine learning (ML) accelerates organometallic catalysis by extracting patterns from complex data. This review details ML applications in optimizing reactions, understanding mechanisms, and discovering new catalysts, reducing experimental effort.

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Area of Science:

  • Chemistry
  • Data Science

Background:

  • Organometallic catalysis is crucial for synthesis but challenging to design and optimize.
  • Vast chemical space, limited data, and complex factors hinder traditional approaches.

Purpose of the Study:

  • Provide chemists with an introduction to machine learning (ML) principles and applications in catalysis.
  • Survey recent advances in ML for organometallic catalysis.

Main Methods:

  • Introduction to ML principles and algorithms.
  • Review of ML applications categorized by function: reaction optimization, mechanistic elucidation, ligand design, stereocontrol, and catalyst discovery.

Main Results:

  • ML effectively extracts patterns and makes predictions in complex chemical systems.
  • Case studies demonstrate ML's ability to reduce experimental workload and enhance mechanistic understanding.
  • ML guides rational catalyst development.

Conclusions:

  • Machine learning offers significant potential to advance organometallic catalysis.
  • Current limitations and future opportunities exist at the intersection of data science and catalysis.