Abstract
A two-tiered strategy for optimizing reaction conditions has been developed for use with an automated chemistry workstation capable of parallel adaptive experimentation. In tier one, a broad survey of conditions is performed in parallel. In tier two, the promising region identified in the first tier is used as the starting point for in-depth searches. The search strategies employed in tier two include the composite-modified Simplex (CMS), multidirectional search (MDS), and parallel Simplex search (PSS) methods. Accordingly, this two-tiered strategy has been integrated into the CMS, MDS, and PSS experiment-planning modules. Each of these methods requires an initial user-defined simplex to explore reaction conditions. The breadth-first survey avoids the lengthy experimentation that occurs when the initial simplex is located far from the optimal region, and also diminishes the possibility of trapping in local maxima. Thus, the two-tiered strategy (breadth-first, depth-second) enables the optimal region to be reached rapidly.
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