ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
main.pdf (20.63 MB)
0/0

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

preprint
submitted on 27.04.2018 and posted on 30.04.2018 by Florian Häse, Loic Roch, Alan Aspuru-Guzik
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.

Funding

Herchel Smith Graduate Fellowship; Tata Sons Limited - Alliance Agreement (A32391); Dr. Anders Frøseth

History

Email Address of Submitting Author

hase.florian@gmail.com

Email Address(es) for Other Author(s)

loic.m.roch@gmail.com alan@aspuru.com

Institution

Harvard University

Country

United States of America

ORCID For Submitting Author

0000-0003-3711-9761

Declaration of Conflict of Interest

No conflict of interest to declare.

Exports