JMP Bayesian Optimization Case Study with Complex Formulation Constraints (2026-US-PO-2981)

Bayesian optimization is a powerful statistical approach that accelerates discovery in molecules, formulations, products, packaging, and processes by efficiently guiding sequential experiments, making it ideal for costly or time-consuming studies. This presentation demonstrates the applications of JMP Bayseian Optimization to optimize product and formulation design. We showcase a complex case study with more than 100 formulation ingredients and many categories of ingredients with both category-level and ingredient-level constraints. By generating a candidate formulation set that satisfied these complex formulation constraints and then applying JMP Bayesian Optimization, we successfully identified and experimentally confirmed improved formulation candidates.