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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,


Accelerated Discovery of Multi-phase Refractory Alloys through Machine Learning Surrogate Models of CALPHAD
Autonomous X-ray Scattering for the Study of Non-equilibrium Self-assembly
Designing Nano-architectured Materials with a Machine-learning Augmented Framework
Discovery of Nanocomposite Phase Change Memory Materials via Closed-loop Autonomous Combinatorial Experimentation
Intelligent Design of Additively Manufactured Architected Materials
Machine Learning Based Hierarchical Multi-scale Modeling of Mechanical Deformation for Metal-matrix-nano-composites
Volumetric Nanoscale Imaging of DNA-assembled Nanoparticle Superlattices
“Big Data” Characterization of Material Properties and High Temperature Kinetics

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