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

A. Gilad Kusne (), Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta and Ichiro Takeuchi ()
Additional contact information
A. Gilad Kusne: National Institute of Standards and Technology
Heshan Yu: University of Maryland
Changming Wu: University of Washington
Huairuo Zhang: National Institute of Standards and Technology
Jason Hattrick-Simpers: National Institute of Standards and Technology
Brian DeCost: National Institute of Standards and Technology
Suchismita Sarker: Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory
Corey Oses: Duke University
Cormac Toher: Duke University
Stefano Curtarolo: Duke University
Albert V. Davydov: National Institute of Standards and Technology
Ritesh Agarwal: University of Pennsylvania
Leonid A. Bendersky: National Institute of Standards and Technology
Mo Li: University of Washington
Apurva Mehta: Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory
Ichiro Takeuchi: University of Maryland

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-19597-w

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