Good optimization feels less like finding an answer and more like navigating uncertainty.
Good optimization feels less like finding an answer and more like navigating uncertainty.
Finding the “Best next experiment” sounds intuitive.
But in real process development, it’s rarely the full story.
Chemists don’t run experiments just to hit a peak. They run them to navigate uncertainty - balancing progress toward better conditions with an understanding of how the system behaves.
The real question usually isn’t: “What experiment maximizes the objective?”
It’s: “What experiment moves us forward while increasing confidence?”
A single high-yield point is interesting. Understanding how sensitive that point is - and where the chemistry remains stable - is what makes it useful.
Pure peak chasing is fragile. Pure mapping without direction is slow.
In practice, good optimisation lives in the space between progress and understanding: moving toward better conditions while learning where the chemistry is robust, sensitive, or likely to fail.
The most valuable experiments aren’t always the ones that improve performance the most.
They’re the ones that reduce uncertainty without stalling momentum.
Seen this way, optimization isn’t about choosing the “best” next experiment. It’s about choosing the most informative step forward - given where you are right now.
At @ReactWise, our goal is to help teams move faster without sacrificing confidence - especially when navigating complex, uncertain process spaces.
