Range-Aware Bayesian Optimization for Discovering Diverse Designs within Target Property Windows
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...
| Authors | Shengli Jiang, Jason Wu, Charles M. Schroeder et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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