Efficient Prediction of Grain Boundary Energies from Atomistic Simulations via Sequential Design

@article{Kroll2022EfficientPO,
  title={Efficient Prediction of Grain Boundary Energies from Atomistic Simulations via Sequential Design},
  author={Martin Kroll and Timo Schmalofski and Holger Dette and Rebecca Janisch},
  journal={Advanced Theory and Simulations},
  year={2022},
  volume={5}
}
With the goal of improving data based materials design, it is shown that by a sequential design of experiment scheme the process of generating and learning from the data can be combined to discover the relevant sections of the parameter space. The application is the energy of grain boundaries as a function of their geometric degrees of freedom, calculated from a simple model, or via atomistic simulations. The challenge is to predict the deep cusps of the energy, which are located at irregular… 

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