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On-the-fly closed-loop materials discovery via Bayesian active learning.

Publication ,  Journal Article
Kusne, AG; Yu, H; Wu, C; Zhang, H; Hattrick-Simpers, J; DeCost, B; Sarker, S; Oses, C; Toher, C; Curtarolo, S; Davydov, AV; Agarwal, R; Li, M ...
Published in: Nature communications
November 2020

Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic impact of scientists being physically separated from their labs. The real-time closed-loop, autonomous system for materials exploration and optimization (CAMEO) is implemented at the synchrotron beamline to accelerate the interconnected tasks of phase mapping and property optimization, with each cycle taking seconds to minutes. We also demonstrate an embodiment of human-machine interaction, where human-in-the-loop is called to play a contributing role within each cycle. This work has resulted in the discovery of a novel epitaxial nanocomposite phase-change memory material.

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Published In

Nature communications

DOI

EISSN

2041-1723

ISSN

2041-1723

Publication Date

November 2020

Volume

11

Issue

1

Start / End Page

5966
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Kusne, A. G., Yu, H., Wu, C., Zhang, H., Hattrick-Simpers, J., DeCost, B., … Takeuchi, I. (2020). On-the-fly closed-loop materials discovery via Bayesian active learning. Nature Communications, 11(1), 5966. https://doi.org/10.1038/s41467-020-19597-w
Kusne, A Gilad, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, et al. “On-the-fly closed-loop materials discovery via Bayesian active learning.Nature Communications 11, no. 1 (November 2020): 5966. https://doi.org/10.1038/s41467-020-19597-w.
Kusne AG, Yu H, Wu C, Zhang H, Hattrick-Simpers J, DeCost B, et al. On-the-fly closed-loop materials discovery via Bayesian active learning. Nature communications. 2020 Nov;11(1):5966.
Kusne, A. Gilad, et al. “On-the-fly closed-loop materials discovery via Bayesian active learning.Nature Communications, vol. 11, no. 1, Nov. 2020, p. 5966. Epmc, doi:10.1038/s41467-020-19597-w.
Kusne AG, Yu H, Wu C, Zhang H, Hattrick-Simpers J, DeCost B, Sarker S, Oses C, Toher C, Curtarolo S, Davydov AV, Agarwal R, Bendersky LA, Li M, Mehta A, Takeuchi I. On-the-fly closed-loop materials discovery via Bayesian active learning. Nature communications. 2020 Nov;11(1):5966.

Published In

Nature communications

DOI

EISSN

2041-1723

ISSN

2041-1723

Publication Date

November 2020

Volume

11

Issue

1

Start / End Page

5966