Convergence of artificial intelligence and experimental automation

Progress in Materials Science Journal

The ever-increasing demand for novel materials with superior properties inspires retrofitting traditional research paradigms in the era of artificial intelligence and automation. An autonomous experimental platform (AEP) has emerged as an exciting research frontier that achieves full autonomy via integrating data-driven algorithms such as machine learning (ML) with experimental automation in the material development loop from synthesis, characterization, and analysis, to decision making. In this review, we started with a primer to describe how to develop data-driven algorithms for solving material problems. Then, we systematically summarized recent progress on automated material synthesis, ML-enabled data analysis, and decision-making. Finally, we discussed the challenges and opportunities in an endeavor to develop the next-generation AEP for ultimately realizing an autonomous or self-driving laboratory. This review will provide insights for researchers aiming to learn the frontier of ML in materials science and deploy AEP in their labs for accelerating material development.

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Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Yunchao Xie, Kianoosh Sattari, Chi Zhang, Jian Lin

Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO 65211, United States

Progress in Materials Science Journal
Volume 132, February 2023, 101043
https://doi.org/10.1016/j.pmatsci.2022.101043

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