The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: XLOOP 2023: The 5th Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing
Authors: Tobias Ginsburg (Argonne National Laboratory (ANL), Data Science and Learning Division); Kyle Hippe and Ryan Lewis (Argonne National Laboratory (ANL)); Aileen Cleary (Northwestern University); Doga Ozgulbas (Argonne National Laboratory (ANL)); Rory Butler (University of Chicago); Casey Stone and Abraham Stroka (Argonne National Laboratory (ANL)); and Rafael Vescovi and Ian Foster (Argonne National Laboratory (ANL), Data Science and Learning Division)
Abstract: Self Driving Labs (SDLs) that combine automation of experimental procedures with autonomous decision making are gaining popularity as a means of increasing the throughput of scientific workflows. The task of identifying a mix of supplied colored pigments that matches a target color, the color matching problem, has emerged as a simple and flexible test case for these labs, as it requires experiment proposal, sample creation, and sample analysis, three common components in automated discovery applications. We present a modular, easily retargetable robotic solution to the color matching problem that allows for fully autonomous execution of a color matching protocol, with feedback from pluggable optimization approaches allowing for continuous refinement and automated publication of results facilitating experiment tracking and post-hoc analysis