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Handbook of Materials Modeling pp 661–680Cite as

Neural Network Potentials in Materials Modeling

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Abstract

The availability of reliable interatomic potentials is necessary for carrying out computer simulations of complex materials. While electronic structure methods like density functional theory have been applied with great success to many systems, the high computational costs of these methods severely restrict the scientific problems that can be studied. Consequently, in recent years a lot of effort has been spent on the development of more efficient potentials enabling large-scale simulations. In particular, machine learning potentials have received considerable attention, because they promise to combine the accuracy of first-principles methods with the efficiency of force fields. In this chapter an important class of machine learning potentials employing artificial neural networks will be reviewed and discussed.

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Author information

Authors and Affiliations

  1. Institut für Physikalische Chemie, Theoretische Chemie, Universität Göttingen, Göttingen, Germany

    Matti Hellström & Jörg Behler

Authors
  1. Matti Hellström
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  2. Jörg Behler
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Corresponding authors

Correspondence to Matti Hellström or Jörg Behler .

Editor information

Editors and Affiliations

  1. Institute of Physics, Swiss Federal Institute of Technology – Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science and Engineering, Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sidney Yip

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Hellström, M., Behler, J. (2020). Neural Network Potentials in Materials Modeling. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-44677-6_56

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