About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Self-organizing Nano-architectured Materials
|
Presentation Title |
H-19: Unveiling Phase Transition in Solid-state Dealloyed Thin Films Using Autonomous Synchrotron X-ray Characterization |
Author(s) |
Cheng-Chu Chung, Chonghang Zhao, Marcus Noack, Kedar Manandhar, Joshua Lynch, Hui Zhong, Ming Lu, Mingzhao Liu, Jianming Bai, Philip Maffettone, Daniel Olds, Masafumi Fukuto, Ichiro Takeuchi, Sanjit Ghose, Thomas Caswell, Kevin Yager, Yu-Chen Karen Chen-Wiegart |
On-Site Speaker (Planned) |
Cheng-Chu Chung |
Abstract Scope |
Thin-film solid-state interfacial dealloying (SSID) recently emerged as an attractive method for forming bi-continuous metal-metal composites and nano-porous metal films. This offers the potential to create nano-architectured materials with a broader range of elemental composition, at a lower processing temperature compared with the liquid metal dealloying. However, the processing-structure relationship in thin-film SSID remains unclear due to the limitations of a large parameter space, such as the parting limit, thin-film thickness, dealloying time, and temperature. In this work, we applied machine learning-augmented methods to analyze the phase compositions resulting from these parameters. Through synchrotron X-ray diffraction (XRD) where the conventional grid scanning, Gaussian process (gpCAM), crystallography companion agent (XCA) methods were tested, we explored the three-dimensional parameter space of potential dealloying systems. Such an autonomous experimental approach can be further combined with other machine learning-driven methods for materials design, providing valuable insights into the thin-film SSID process. |
Proceedings Inclusion? |
Undecided |
Keywords |
Thin Films and Interfaces, Machine Learning, Nanotechnology |