Bayesian optimization for chemical products and functional materials

The design of chemical-based products and functional materials is vital to modern technologies, yet remains expensive and slow. Artificial intelligence and machine learning offer new approaches to leverage data to overcome these challenges. This review focuses on recent applications of Bayesian optimization (BO) to chemical products and materials including molecular design, drug discovery, molecular modeling, electrolyte design, and additive manufacturing. Numerous examples show how BO often requires an order of magnitude fewer experiments than Edisonian search. The essential equations for BO are introduced in a self-contained primer specifically written for chemical engineers and others new to the area. Finally, the review discusses four current research directions for BO and their relevance to product and materials design.

Automated summary

This review discusses the importance of artificial intelligence and machine learning in the design of chemical-based products and functional materials. It highlights the recent applications of Bayesian optimization in various areas such as molecular design, drug discovery, and additive manufacturing, showcasing its efficiency compared to traditional search methods. Additionally, it introduces the essential equations for Bayesian optimization and current research directions in the field.