| 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 |
Designing Nano-architectured Materials with a Machine-learning Augmented Framework |
| Author(s) |
Chonghang Zhao, Cheng-Chu Chung, Yu-chen Karen Chen-Wiegart |
| On-Site Speaker (Planned) |
Yu-chen Karen Chen-Wiegart |
| Abstract Scope |
Design parameter space for nano-architectured materials is large and complex. Even with the guidance of simulation and theoretical computation, creating nano-architectured materials, despite being at the forefront of materials science, could suffer from the common issue of intuition-driven “trial-and-error” materials discovery. While there has been a significant increase in developing data-driven, machine-learning (ML) based methods for efficient materials design, only recently have researchers begun to integrate these approaches with advanced, high-throughput characterization tools. This talk aims to provide an overview on the latest development of this exciting direction in materials science and engineering. A use case of solid-state interfacial dealloying (SSID) creating bicontinuous nanostructures will also be presented; the example will highlight the use of ML-based models and algorithms to predict materials’ synthesibility as well as the promise to apply automated and autonomous experimental approaches at synchrotron facilities to further enhance and validate the ML models. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Machine Learning, Nanotechnology, Thin Films and Interfaces |