Benchmarking the Acceleration of Materials Discovery by Sequential Learning

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.

Routescore: Punching the Ticket to More Efficient Materials Development

“…Recently, several studies have highlighted the efficiency of ML-driven experiment planners for achieving inverse design. [45][46][47][48][49][50][51] We follow suit for our simulated MAP by quantitatively comparing its aptitude for identifying synthetically feasible laser molecules to that of a simple random sampling strategy. Here we consider the following question: what fraction of total satisfactory molecules can each strategy identify given a budget of 500 evaluations (Figure S14)?…”