Democratizing experimental benchmarking of materials discovery campaigns through a low-cost, self-driving optics demo

By Sterling G. Baird

self-driving laboratory bayesian optimization autonomous research demonstration pedagogy

Self-driving labs are the future; however, the capital and expertise required can be daunting. We introduce the idea of a constrained, high-dimensional, multi-objective optimization task for less than $100, a square foot of desk space, and an hour of total setup time from the shopping cart to the first "autonomous drive." We use optics rather than chemistry for our demo; after all, light is easier to move than matter. While not strictly materials-based, importantly, several core principles of a self-driving materials discovery lab are retained in this cross-domain example: sending commands to hardware to adjust physical parameters, receiving measured objective properties, decision-making via active learning, and utilizing cloud-based simulations. The demo is accessible, extensible, modular, and repeatable, making it an ideal candidate for both low-cost experimental adaptive design benchmarking and learning the principles of self-driving laboratories in a low-risk setting.

Attachment: abstract.png (223 KB)

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