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

30 April 2018, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

Pareto optimization
Self-driving laboratories
Autonomous discovery
Reaction optimization
Achievement scalarizing function
Machine learning

Supplementary weblinks

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.