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.
@article{arxiv.2206.07108,
title = {A Framework for the Optimal Selection for High-Throughput Data Collection Workflows by Autonomous Experimentation Systems},
author = {Rohan Casukhela and Sriram Vijayan and Joerg R. Jinschek and Stephen R. Niezgoda},
journal= {arXiv preprint arXiv:2206.07108},
year = {2022}
}