[shoo]

Experimental settings like these are also interesting because they provide opportunities for the application of active learning.

For example, if we wish to learn the properties of some family of new materials, we may have to choose which particular elements of the family to synthesise before we can begin measuring anything. Even if we can only afford to take 100 samples, or less, the synthesis procedure might have a dozen parameters or more.

We then have the problem of sensibly selecting a small number of samples from a relatively high dimensional space. In this situation it could be very easy to justify some serious computational effort if it could potentially save months of wasted effort in the lab.