AROPS: A Framework of Automated Reaction Optimization with Parallelized Scheduling

With the development of automated experimental platforms and optimization algorithms, chemists can easily optimize chemical reactions in an automated and high-throughput fashion. However, the modules in existing automated experimental platforms are operated in a predefined way without orchestrating with the optimization algorithm, thus leaving room for further efficiency improvement. Here, we introduced a framework of automated reaction optimization with parallelized scheduling (AROPS) to realize the integration of optimization algorithm and module scheduling. AROPS relies on a customized Bayesian optimizer to solve multi-reactor/analyzer reaction optimization problems with three different scheduling modes to arrange tasks for various experimental modules. In addition, a mechanism based on probability of improvement (PI) for discarding unpromising on-going experiments was developed to facilitate freeing-up valuable experimental resources in parallelized optimization. We tested the performance of AROPS using a hardware emulator on three typical benchmark reactions encountered in organic synthesis, illustrating that AROPS can trade off optimization time and cost according to the chemists’ preference.