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.

翻译：自驱动实验室包含多个执行材料合成与表征任务的站点。为最小化站点停机时间并最大化实验通量，实践中采用异步并行方式运行实验，使得多个实验在不同阶段同时进行。然而，实验的异步并行化会引入延迟反馈（即"待处理实验"），这已被证实会降低贝叶斯优化器的性能。本文构建了一个多阶段自驱动实验室模拟器，并比较了应对延迟反馈与异步并行操作的优化策略。利用真实自驱动实验室的数据，我们基于177个先前运行的功能涂层电导率最大化实验，构建了一个真实贝叶斯优化模拟器。随后比较了期望提升、噪声期望提升、4模式探索和随机采样等搜索策略，通过延迟量和问题维度评估其性能。仿真结果揭示了异步并行操作与延迟反馈之间的权衡关系。