  Self-driving laboratories (SDLs) consist of multiple stations that perform
material synthesis and characterisation tasks. To minimize station downtime and
maximize experimental throughput, it is practical to run experiments in
asynchronous parallel, in which multiple experiments are being performed at
once in different stages. Asynchronous parallelization of experiments, however,
introduces delayed feedback (i.e. "pending experiments"), which is known to
reduce Bayesian optimiser performance. Here, we build a simulator for a
multi-stage SDL and compare optimisation strategies for dealing with delayed
feedback and asynchronous parallelized operation. Using data from a real SDL,
we build a ground truth Bayesian optimisation simulator from 177 previously run
experiments for maximizing the conductivity of functional coatings. We then
compare search strategies such as expected improvement, noisy expected
improvement, 4-mode exploration and random sampling. We evaluate their
performance in terms of amount of delay and problem dimensionality. Our
simulation results showcase the trade-off between the asynchronous parallel
operation and delayed feedback.
