|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Inference, Uncertainty Quantification, and Uncertainty Propagation for Grain Boundary Structure-property Models
||Oliver Johnson, Eric R Homer, David T Fullwood, David E Page, Kathryn F Varela, Sterling G Baird
|On-Site Speaker (Planned)
We present a non-parametric Bayesian approach for developing structure-property models for grain boundaries (GBs) with built-in uncertainty quantification (UQ). Using this method we infer a structure-property model for H diffusivity in  tilt GBs in Ni at 700K based on molecular dynamics (MD) data. We then leverage these results to perform uncertainty propagation (UP) for mesoscale simulations of the effective diffusivity of polycrystals to investigate the interaction between structure-property model uncertainties and GB network structure. We observe a fundamental interaction between crystallographic correlations and spatial correlations in GB networks that causes certain types of microstructures (those with large populations of J2- and J3-type triple junctions) to exhibit intrinsically larger uncertainty in their effective properties.
||Other, Machine Learning, Other