Bayesian Optimization for Chemical Reactions.

Area of Science:

• Chemistry
• Machine Learning
• Computational Science

Background:

• Chemical reaction optimization is traditionally resource-intensive, often requiring hundreds of experiments guided by domain experts.
• Complex reaction landscapes pose significant challenges, leading to substantial time and material costs.

Purpose of the Study:

• To review the application of Bayesian optimization (BO) for optimizing chemical reactions.
• To discuss the transformation of chemical reactions into machine-readable formats for machine learning (ML) models.
• To highlight the potential of ML-augmented reaction optimization.

Main Methods:

• Bayesian optimization (BO) as an iterative algorithm that uses previous experimental data to suggest the next optimal experiment.
• Machine learning (ML) models trained on machine-readable representations of chemical reactions.
• Review of existing literature on BO applications in chemical reaction optimization.

Main Results:

• BO has demonstrated increased efficiency in chemical optimization campaigns.
• BO can effectively recommend favorable reaction conditions and jointly optimize multiple objectives like yield and stereoselectivity.
• The accessibility of BO software has lowered the barrier for its application in chemistry.

Conclusions:

• Bayesian optimization offers a powerful, efficient alternative or complement to traditional expert-guided reaction optimization.
• The integration of ML and BO holds significant promise for transforming chemical synthesis and discovery.
• Effective ML-augmented reaction optimization necessitates close collaboration between experimental chemists and computational scientists.