Performance of Search Algorithms in the Examination of Chemical Reaction Spaces with an Automated Chemistry Workstation

Abstract

One of the challenges in applying automated chemistry workstations to problems of reaction optimization entails choosing an appropriate optimization algorithm. In the study described herein, 10 different algorithms have been examined for efficacy in searching reaction spaces using scenarios that explore effects of workstation parallelism and search space size. The algorithms differ in scheduling (serial vs. parallel), adaptive features (open loop vs. closed loop), and methods for stepping through the search space. Several two-tiered algorithms enable a breadth-first survey followed by an indepth optimization. For a workstation with modest parallel capacity, a parallel but nonadaptive algorithm is most effective in small or coarse-grained search spaces, whereas parallel adaptive algorithms are superior for examining large or fine-grained search spaces. The parallel adaptive algorithms become increasingly effective as the size of the search space increases. A serial algorithm is most attractive with a serial workstation, or when chemical resources are limited regardless of workstation or search space. The breadth-first survey of the twotiered algorithms significantly improves the efficiency of the subsequent in-depth optimization. The results obtained provide guidance in choosing optimization algorithms, designing more sophisticated algorithms, and developing workstations with parallel and/or adaptive features that use such algorithms.