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benchmark_seq_learning_chemrxiv_submit1.pdf (7.24 MB)

Benchmarking the Acceleration of Materials Discovery by Sequential Learning

submitted on 02.12.2019, 17:46 and posted on 16.12.2019, 23:37 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.


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California Institute of Technology


United States

ORCID For Submitting Author


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.