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

Autonomous experimentation systems have been used to greatly advance the Integrated Computational Materials Engineering 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 highest-value information as per a user-defined objective.

We employ this framework to select the optimal high-throughput workflow for the characterization of an additively manufactured Ti–6Al–4V sample using a deep-learning based image denoiser. The selected workflow reduced the collection time of backscattered electron scanning electron microscopy images by a factor of 5 times as compared to the case study’s benchmark workflow, and by a factor of 85 times as compared to the workflow used in a previously published study.