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N. Sahinidis

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Open access Jul 2026

Data-driven optimization: efficient adaptive learning for self-driving laboratories

It is shown how moving from BO to partitioning-based algorithms can substantially improve data efficiency, scale gracefully to dozens of process variables, and handle the constraints and noise that characterize realistic experimental campaigns.

N. Sahinidis · 0 citations