Range-Aware Bayesian Optimization for Discovering Diverse Designs within Target Property Windows
For materials and product designers, this work provides a sample-efficient BO framework that handles multiple property specifications and promotes solution diversity, addressing a practical need for flexible design.
Range-aware Bayesian optimization (BO) directly scores the posterior probability that a candidate satisfies a target property range, enabling discovery of diverse designs within acceptable windows. It recovers larger and more diverse valid designs than standard BO and goal-seeking methods, demonstrated on benchmarks and two case studies (polymer synthesis and oligomer discovery).
In many materials and product design problems, desirable candidates exhibit properties that fall within an acceptable range rather than achieve a single optimum. Recovering multiple, distinct solutions that satisfy such specifications is also practically valuable, as some candidates may be preferred for reasons of cost, processability, or robustness that are difficult to encode directly in an objective function. Here, we develop a range-aware Bayesian optimization (BO) framework in which the acquisition function directly scores the posterior probability that a candidate satisfies a target range. The framework naturally extends to parallel pursuit of multiple distinct specifications over a shared candidate space. Across benchmark tasks, range-aware acquisition consistently recovers larger and more diverse sets of valid designs than standard BO baselines and recent goal-seeking methods. Its utility is further demonstrated in two practically motivated design case studies involving optimizing reaction conditions for polymer synthesis and sequence-defined oligomer discovery for prescribed optical absorption bands, supported by quantum chemical calculations. These results suggest that range-aware BO can provide a practical and sample-efficient foundation for specification-driven design, particularly when design flexibility and solution diversity are important considerations.