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Autonomous experimentation systems have been used to greatly advance theintegrated computational materials engineering (ICME) paradigm. This paperoutlines a framework that enables the design and selection of data collectionworkflows for autonomous experimentation systems. The framework first searchesfor data collection workflows that generate high-quality information and thenselects the workflow that generates the \emph{best, highest-value} informationas per a user-defined objective. We employ this framework to select the\emph{user-defined best} high-throughput workflow for material characterizationon an additively manufactured Ti-6Al-4V sample for the purposes of outlining abasic materials characterization scenario, reducing the collection time ofbackscattered electron scanning electron scanning electron microscopy images bya factor of 5 times as compared to the benchmark workflow for the case studypresented, and by a factor of 85 times as compared to the workflow used in thepreviously published study.

A Framework for the Optimal Selection for High-Throughput Data Collection Workflows by Autonomous Experimentation Systems