Search Strategies for Self-driving Laboratories with Pending Experiments

Self-driving laboratories (SDLs) consist of multiple stations that perform materialsynthesis and characterisation tasks. To minimize station downtime and maxi-mize experimental throughput, it is practical to run experiments in asynchronousparallel, in which multiple experiments are being performed at once in differ-ent stages. Asynchronous parallelization of experiments, however, introducesdelayed feedback (i.e. “pending points”), which is known to reduce Bayesianoptimizer performance. Here, we build a simulator for a multi-stage SDL and com-pare optimization strategies for dealing with delayed feedback and asynchronousparallelized operation. Using data from [1], we build a ground truth Bayesianoptimization simulator from 177 previously run experiments for maximizing theconductivity of functional coatings. We then compare search strategies such asnaive expected improvement, 4-mode exploration as proposed by the originalauthors and asynchronous batching. We evaluate their performance in terms ofnumber of stages, and short, medium and long-term optimization performance.Our simulation results showcase the trade-off between the asynchronous paralleloperation and delayed feedback.