On-the-fly closed-loop materials discovery via Bayesian active learning.

ABSTRACT: Active learning-the field of machine learning (ML) dedicated to optimal experiment design-has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics. In this work, we focus a closed-loop, active learning-driven autonomous system on another major challenge, the discovery of advanced materials against the exceedingly complex synthesis-processes-structure-property landscape. We demonstrate an autonomous materials discovery methodology for functional inorganic compounds which allow scientists to fail smarter, learn faster, and spend less resources in their studies, while simultaneously improving trust in scientific results and machine learning tools. This robot science enables science-over-the-network, reducing the economic i

SUBMITTER: Kusne AG 

PROVIDER: S-EPMC7686338 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature