Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories

We introduce Chimera, a general purpose achievement scalarizing function (ASF) for multi-objective optimization problems in experiment design. Chimera combines concepts of a priori scalarizing with ideas from lexicographic approaches. It constructs a single merit-based function which implicitly accounts for a provided hierarchy in the objectives. The performance of the suggested ASF is demonstrated on several well-established analytic multi-objective benchmark sets using different single-objective optimization algorithms. We further illustrate the performance and applicability of Chimera on two practical applications: (i) the auto-calibration of a virtual robotic sampling sequence for direct-injection, and (ii) the inverse-design of a system for efficient excitation energy transport. The results indicate that Chimera enables a wide class of optimization algorithms to rapidly find solutions. The presented applications highlight the interpretability of Chimera to corroborate design choices on tailoring system parameters. Additionally, Chimera appears to be applicable to any set of n unknown objective functions, and more importantly does not require detailed knowledge about these objectives. We recommend the use of Chimera in combination with a variety of optimization algorithms for an efficient and robust optimization of multi-objective problems.