Empowering scientists with data-driven automated experimentation

NATURE SYNTHESIS（2023）

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Abstract

Automated experiments with integrated characterization techniques greatly accelerate materials synthesis and provide data to be used by machine learning algorithms. We reflect on the current use of data-driven automated experimentation in materials synthesis and consider the future of this approach.

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