How can Bayesian optimization help a wet lab without big workflow changes?

[Donald_G]

I'm setting up a series of material synthesis experiments, and the parameter space is huge. I've read about bayesian optimization for scientific experiments, but I'm honestly a bit intimidated by the implementation. My lab mostly just does one-factor-at-a-time testing, and I'm worried that suggesting a shift to this more complex approach will just seem like I'm overengineering things. Has anyone here actually tried to introduce something like this into a traditional wet lab setting?

[Emily_R]

I tried bayesian optimization in a small pilot with a wet lab and it helped when I kept the design lean one variable at a time first and then a few guided iterations.

[NatalieL]

I am skeptical that bayesian optimization will save you in the lab it can add complexity that feeds into over engineering and more charts to chase.

[AbigailB]

A cautious path could be to treat bayesian optimization as a learning aid rather than a replacement for intuition, start with a low dimensional space and compare results with your one factor at a time tests.

[MadisonBG]

Have you defined a clear objective for the optimization such as yield or purity and how will you handle noise in measurements when applying bayesian optimization?

[MilaVP]

A few colleagues did a small bayesian optimization run to pick between a handful of catalysts and saw a speed up in early exploration but they stopped once the space grew and measurement noise crept in.

[AlexanderHT]

If you try it share what you learn about the workflow and data capture because the real test is whether the team actually uses it day to day when the pipeline is busy.