About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
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Symposium
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Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
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Presentation Title |
Discovery of Nanocomposite Phase Change Memory Materials via Closed-loop Autonomous Combinatorial Experimentation |
Author(s) |
Ichiro Takeuchi |
On-Site Speaker (Planned) |
Ichiro Takeuchi |
Abstract Scope |
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. By incorporating active learning into the protocol of combinatorial characterization, we can streamline the measurement and the analysis process substantially. I will describe how we have discovered 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. This work is performed in collaboration with A. Gilad Kusne, H. Yu, M. Li, E. Pop, and A. Mehta. This work is funded by SRC, ONR, and NIST. |
Proceedings Inclusion? |
Undecided |
Keywords |
Electronic Materials, Machine Learning, Thin Films and Interfaces |