  Autonomous experimentation systems have been used to greatly advance the
integrated computational materials engineering (ICME) paradigm. This paper
outlines a framework that enables the design and selection of data collection
workflows for autonomous experimentation systems. The framework first searches
for data collection workflows that generate high-quality information and then
selects the workflow that generates the \emph{best, highest-value} information
as per a user-defined objective. We employ this framework to select the
\emph{user-defined best} high-throughput workflow for material characterization
on an additively manufactured Ti-6Al-4V sample for the purposes of outlining a
basic materials characterization scenario, reducing the collection time of
backscattered electron scanning electron scanning electron microscopy images by
a factor of 5 times as compared to the benchmark workflow for the case study
presented, and by a factor of 85 times as compared to the workflow used in the
previously published study.

Materials Science;Computational Physics