未完了実験を考慮した自己駆動型実験室の探索戦略
Search Strategies for Self-driving Laboratories with Pending Experiments
多段階の自己駆動型実験室において、非同期並列実験による遅延フィードバックがベイズ最適化性能に与える影響をシミュレータで評価し、探索戦略を比較した。
著者: Hao Wen, Jakob Zeitler, Connor Rupnow
分類: cs.LG, cs.RO
原文アブストラクト
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.
