These are preliminary reports that have not been peer-reviewed.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.
Benchmarking the Acceleration of Materials Discovery by Sequential Learning
preprintsubmitted 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.
Read the published paper
in Chemical Science