A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems
Source abstract
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.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Scientific Computing and Data Management · Machine Learning in Materials Science · Advanced X-ray and CT Imaging
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References (41)
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