| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Frontiers of Materials Award Symposium Session: Data-Driven, Machine-learning Augmented Design and Novel Characterization for Nano-architectured Materials
|
| Presentation Title |
Autonomous X-ray Scattering for the Study of Non-equilibrium Self-assembly |
| Author(s) |
Kevin Yager |
| On-Site Speaker (Planned) |
Kevin Yager |
| Abstract Scope |
Block copolymer thin films self-assemble into canonical morphologies. The diversity of structures can be increased by leveraging non-equilibrium effects. For instance, pathway-dependent aspects of assembly can be exploited to stabilize non-native motifs. However, this greatly increases the corresponding material discovery task, since the space of possible processing pathways is enormous. Autonomous experimentation approaches, which leverage machine-learning to drive a measurement loop and optimally explore a given problem, are a potential solution to this enormous challenge. This talk will discuss the ongoing development of autonomous experimentation at a synchrotron x-ray scattering beamline, using non-equilibrium block copolymer assembly as a key example. |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Machine Learning, Thin Films and Interfaces, |