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Empowering scientists with data-driven automated experimentation

Nature Synthesis volume 2, pages 462–463 (2023)Cite this article

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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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Fig. 1: Operation workflow of data-driven automated experiments.

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Acknowledgements

The authors acknowledge support from the National Science Foundation (NSF), award no. 2043205, and the Alfred P. Sloan Foundation, award no. FG-2022-18275.

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  1. Institute for Advanced Materials and Manufacturing, Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN, USA

    Jonghee Yang & Mahshid Ahmadi

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  1. Jonghee Yang
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  2. Mahshid Ahmadi
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Correspondence to Mahshid Ahmadi.

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The authors declare no competing interests.

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Yang, J., Ahmadi, M. Empowering scientists with data-driven automated experimentation. Nat. Synth 2, 462–463 (2023). https://doi.org/10.1038/s44160-023-00337-z

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