Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Xie, Yunchao; Sattari, Kianoosh; Zhang, Chi; Lin, Jian

doi:10.1016/j.pmatsci.2022.101043

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Journal Article · Thu Nov 10 23:00:00 EST 2022 · Progress in Materials Science

DOI:https://doi.org/10.1016/j.pmatsci.2022.101043· OSTI ID:2418248

Xie, Yunchao ^[1]; Sattari, Kianoosh ^[2]; Zhang, Chi ^[2]; ^[2]

Univ. of Missouri, Columbia, MO (United States); OSTI
Univ. of Missouri, Columbia, MO (United States)

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. In conclusion, 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.

View Accepted Manuscript (DOE)

Research Organization:: Universal Matter Inc., Houston, TX (United States); National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE Office of Fossil Energy (FE); National Science Foundation (NSF); US Army Corps of Engineers

Grant/Contract Number:: FE0031988

OSTI ID:: 2418248

Alternate ID(s):: OSTI ID: 1899740

Journal Information:: Progress in Materials Science, Journal Name: Progress in Materials Science Journal Issue: C Vol. 132; ISSN 0079-6425

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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