Benchmarking the Acceleration of Materials Discovery by Sequential Learning

16 December 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

active learning
autonomous science
oxygen evolution reaction

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