1. Autonomous materials synthesis via hierarchical active learning of nonequilibrium phase diagrams
- Author
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Sebastian Ament, Maximilian Amsler, Duncan R. Sutherland, Ming-Chiang Chang, Dan Guevarra, Aine B. Connolly, John M. Gregoire, Michael O. Thompson, Carla P. Gomes, and R. Bruce van Dover
- Subjects
Multidisciplinary ,Computer Science ,Materials Science ,SciAdv r-articles ,Physical and Materials Sciences ,02 engineering and technology ,010402 general chemistry ,021001 nanoscience & nanotechnology ,0210 nano-technology ,01 natural sciences ,0104 chemical sciences ,Research Article - Abstract
Description, Artificial intelligence accelerates the search and discovery of new metastable materials for energy applications., 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 models that incorporate the underlying physics of the experiments and end-to-end uncertainty quantification. We demonstrate SARA’s performance by autonomously mapping synthesis phase boundaries for the Bi2O3 system, leading to orders-of-magnitude acceleration in the establishment of a synthesis phase diagram that includes conditions for stabilizing δ-Bi2O3 at room temperature, a critical development for electrochemical technologies.
- Published
- 2021