Decision-tree programs for an adaptive automated chemistry workstation. Application to catalyst screening experiments

Abstract

Automated chemistry workstations with the capability of altering the course of experimentation based on acquired data are essential for performing strategic searches. Such adaptive experimentation requires the ability to compose experimental plans for initiating, monitoring, evaluating, and making decisions about experiments. We have developed a decision-tree (DT) experiment-planning module that includes a complete command set for adaptive operation of the workstation. Functions for dialog prompts are included, enabling generic programs to be tailored for specific applications by various users. The DT experiment-planning module is one component of an experiment planner for an automated microscale chemistry workstation. The DT functions are also used to compose early-termination programs and describe objective functions for use in the companion factorial design, composite-modified simplex, and multidirectional search experiment-planning modules. The features of the DT experiment-planning module are illustrated with a program that screens a list of candidate compounds for catalytic activity. A set of dialog boxes at the beginning of the program prompts the user for information. Based on this information, the candidates are examined automatically (in a serial process) in exhaustive or hill-climbing searching strategies. A yield vs. time pattern-matching procedure is performed dynamically in order to assess whether monitoring should be continued. Decisions also are made concerning examination of the candidate at higher or lower concentrations. Higher concentrations are achieved by addition of aliquots of the candidate to the ongoing reaction, while lower concentrations are achieved by spawning a new reaction. When a user-defined yield threshold is reached or the entire user-specified concentration range has been spanned, catalysts and their effective concentration ranges are identified. This experiment-planning module enables composition of flexible programs for adaptive experimentation that can be used as stand-alone programs or as decision-making components of other searching programs.