Multi-fidelity machine learning models for accurate bandgap predictions of solids

@article{Pilania2017MultifidelityML,
  title={Multi-fidelity machine learning models for accurate bandgap predictions of solids},
  author={G. Pilania and J. Gubernatis and T. Lookman},
  journal={Computational Materials Science},
  year={2017},
  volume={129},
  pages={156-163}
}
Abstract We present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. In addition, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. Using a set of 600 elpasolite compounds as an… Expand
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