Fig. 1: Closed-loop workflow of the EGBO-guided HTE platform. | npj Computational Materials

Fig. 1: Closed-loop workflow of the EGBO-guided HTE platform.

From: Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

Fig. 1

The illustration on the top left of the figure shows the unconstrained (blue dashed line) and constrained (red solid line) Pareto Optimal Set (POS) in a constrained decision space, and its respective projection as the Pareto Front (PF) in the objective space. EGBO algorithm (left) combines an evolutionary algorithm (orange) and a qNEHVI-BO (blue) working in parallel to suggest 4 optimal candidates for the cMOOP. The optimizer’s goal is to (1) efficiently reach the PF, (2) uniformly explore the PF and (3) avoid infeasible domains near the PF (top left). The candidates are then sampled on a hyperspectral HTE platform optimizing AgNP synthesis (right) and further analysis is done to derive the objective values (bottom) before a new EGBO iteration.

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