Closed-loop autonomous combinatorial experimentation for streamlined materials discovery

I. Takeuchi
University of Maryland,
United States

Keywords: AI, materials design, materials discovery


We are incorporating active learning in screening of combinatorial libraries of functional materials. The array format with which samples of different compositions are laid out on combinatorial libraries is particularly conducive to active learning driven autonomous experimentation. For some physical properties, each characterization/measurement requires time/resources long/large enough that true "high"-throughput measurement is not possible. Examples include detection of martensitic transformation and superconducting transitions in thin film libraries. By incorporating active learning into the protocol of combinatorial characterization, we can streamline the measurement and the analysis process substantially. We have previously demonstrated discovery of a new phase change memory (PCM) material using the closed-loop autonomous materials exploration and optimization (CAMEO) strategy. The discovered PCM material has been tested in various scaled-up device formats and continues to exhibit superior performance to industrial standards. Recent efforts in developing synthesis – measurement closed loops on a combinatorial thin film platform will be discussed. This work is performed in collaboration with A. Gilad Kusne, V. Stanev, H. Yu, H. Liang, M. Li, E. Pop, and A. Mehta. This work is funded by SRC, ONR, AFOSR, and NIST.