Autonomous Combinatorial Experimentation

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 will discuss some of our latest efforts including real-time autonomous experiment-theory interaction for closed-loop mapping of thin film phase diagrams, and multi-instrument autonomous characterization of library wafers where two different physical properties are simultaneously mapped. Our effort in developing synthesis – measurement closed loops on a combinatorial thin film platform will also be discussed. This work is performed in collaboration with A. Gilad Kusne, H. Liang, A. McDannald, H. Yu, C.-H. Lee, and M. Lippmaa. This work is funded by SRC, ONR, AFOSR, and NIST.