Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams.

ABSTRACT: Autonomous experimentation enabled by artificial intelligence offers a new paradigm for accelerating scientific discovery. Nonequilibrium materials synthesis is emblematic of complex, resource-intensive experimentation whose acceleration would be a watershed for materials discovery. We demonstrate accelerated exploration of metastable materials through hierarchical autonomous experimentation governed by the Scientific Autonomous Reasoning Agent (SARA). SARA integrates robotic materials synthesis using lateral gradient laser spike annealing and optical characterization along with a hierarchy of AI methods to map out processing phase diagrams. Efficient exploration of the multidimensional parameter space is achieved with nested active learning cycles built upon advanced machine learning mode

SUBMITTER: Ament S 

PROVIDER: S-EPMC8682983 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature