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Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Characterizing GB Atomic Structures at Multiple Scales
Author(s)	Eric R. Homer, Derek M Hensley, Conrad W Rosenbrock, Andrew H Nguyen, Jonathan L Priedeman, Gus L W Hart
On-Site Speaker (Planned)	Eric R. Homer
Abstract Scope	The atomic structure of grain boundaries plays a defining but poorly understood role in the properties they exhibit. Due to the complex nature of these structures, machine learning is a natural tool for extracting meaningful relationships and new physical insight. We present efforts to characterize the GB atomic structure at different length-scales using a few different methods. These include the smooth overlap of atomic positions (SOAP), local environment representation (LER) and a new structural representation, called the scattering transform. The scattering transform uses wavelet-based convolutional neural networks to characterize the complete three-dimensional atomic structure of a grain boundary. The success of the various metrics is evaluated by machine learning to predict GB energy, mobility, and shear coupling. A discussion of the advantages and disadvantages of the various methods is discussed.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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