Guided Discovery: Combining Bayesian Optimization with DOE for Material Discovery and Continuous Improvement

Guided Discovery: Combining Bayesian Optimization with DOE for Material Discovery and Continuous Improvement

Bayesian Optimization is an approach that helps speed up the discovery of new materials by guiding experiments toward the most promising areas in an efficient and strategic way. The method balances experimentation in unexplored spaces as well as high-performing regions to accelerate discovery. In this talk, we will provide an accessible overview of how Bayesian Optimization works and how it can be combined with traditional Design of Experiments (DOE) methods. We will also discuss software options available for implementing this approach. Key statistical ideas that support well-designed and informative experiments throughout the discovery process will be highlighted. By combining these approaches, we can enhance materials discovery and continuous improvement and gain deeper insights into material behavior, helping to drive faster innovation in materials science.

Speakers