About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Predicting Grain Boundary Properties Using Strain Functional Descriptors and Supervised Machine Learning |
Author(s) |
Avanish Mishra, Sumit A Suresh, Khanh Q Dang, Saryu J Fensin, Edward M Kober, Nithin Mathew |
On-Site Speaker (Planned) |
Avanish Mishra |
Abstract Scope |
Properties of grain boundaries (GBs) are controlled by the local atomic environments at the boundary. Although it is possible to estimate properties for well-defined GBs, extrapolating these understanding to real GBs is a formidable task. A significant challenge in establishing desired structure-property relationships is the absence of physically meaningful set of descriptors. Existing descriptor sets can estimate selected properties; however, they fail to provide any physical insights. Strain functional descriptors (SFD), a complete and symmetry-adapted set of descriptors, are ideal for characterizing the local atomic environment at GBs and provides the capability to decompose the atomic arrangement in terms of physically meaningful descriptions such as strains, strain-gradients, and higher order deformations. The presentation will show the application of SFDs for predicting various properties, such as grain boundary energy and atomic energy density on a large database of metastable GBs in Cu and application on polycrystals, using supervised machine learning models. |
Proceedings Inclusion? |
Planned: |
Keywords |
Characterization, Machine Learning, Modeling and Simulation |