These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.
submitted on 02.12.2019 and posted on 16.12.2019by 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.
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.