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

TL;DR

This paper presents a framework for selecting optimal data collection workflows in autonomous experimentation systems, significantly reducing material characterization time while maximizing information quality.

Contribution

It introduces a systematic approach to design and select high-value data workflows tailored to user-defined objectives in autonomous experiments.

Findings

Reduced SEM imaging time by 5x compared to benchmark workflow.

Achieved 85x faster characterization than previous methods.

Demonstrated effectiveness in a Ti-6Al-4V additively manufactured sample.

Abstract

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.