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Benchmarking the Acceleration of Materials Discovery by Sequential Learning

preprint
submitted on 02.12.2019 and posted on 16.12.2019 by Brain Rohr, Helge Stein, Dn Guevarra, Yu Wang, Joel Haber, Muratahan Aykol, Santosh Suram, John Gregoire
Sequential learning (SL) strategies, i.e. iteratively updating a ma-chine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any "good" material, discovery of all "good" materials, and discovery of a model that accurately predicts the performance of new materials.

History

Email Address of Submitting Author

gregoire@caltech.edu

Institution

California Institute of Technology

Country

United States

ORCID For Submitting Author

0000-0002-2863-5265

Declaration of Conflict of Interest

B.R., H.S., S.S. and J.G. filed a provisional patent application on active learning enabled experimental catalyst materials discovery: US App. No. 62/837,379. The remaining authors declare no competing interests.

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