• Corpus ID: 211010416

Constrained Bayesian Nonparametric Regression for Grain Boundary Energy Predictions

@article{Wang2020ConstrainedBN,
  title={Constrained Bayesian Nonparametric Regression for Grain Boundary Energy Predictions},
  author={Haoyu Wang and Srikanth Patala and Brian J. Reich},
  journal={arXiv: Computational Physics},
  year={2020}
}
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… 

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