Constrained Bayesian Nonparametric Regression for Grain Boundary Energy Predictions
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Grain boundary (GB) energy is a fundamental property that affects the form of grain boundary and plays an important role to unveil the behavior of polycrystalline materials. With a better understanding of grain boundary energy distribution (GBED), we can produce more durable and efficient materials that will further improve productivity and reduce loss. The lack of robust GB structure-property relationships still remains one of the biggest obstacles towards developing true bottom-up models for the behavior of polycrystalline materials. Progress has been slow because of the inherent complexity associated with the structure of interfaces and the vast five-dimensional configurational space in which they reside. Estimating the GBED is challenging from a statistical perspective because there are not direct measurements on the grain boundary energy. We only have indirect information in the form of an unidentifiable homogeneous set of linear equations. In this paper, we propose a new statistical model to determine the GBED from the microstructures of polycrystalline materials. We apply spline-based regression with constraints to successfully recover the GB energy surface. Hamiltonian Monte Carlo and Gibbs sampling are used for computation and model fitting. Compared with conventional methods, our method not only gives more accurate predictions but also provides prediction uncertainties.
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