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Phys Chem Chem Phys. 2011 Oct 28;13(40):17930-55. doi: 10.1039/c1cp21668f. Epub 2011 Sep 13.

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations.

Author information

1
Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, 44780 Bochum, Germany. joerg.behler@theochem.ruhr-uni-bochum.de

Abstract

The accuracy of the results obtained in molecular dynamics or Monte Carlo simulations crucially depends on a reliable description of the atomic interactions. A large variety of efficient potentials has been proposed in the literature, but often the optimum functional form is difficult to find and strongly depends on the particular system. In recent years, artificial neural networks (NN) have become a promising new method to construct potentials for a wide range of systems. They offer a number of advantages: they are very general and applicable to systems as different as small molecules, semiconductors and metals; they are numerically very accurate and fast to evaluate; and they can be constructed using any electronic structure method. Significant progress has been made in recent years and a number of successful applications demonstrate the capabilities of neural network potentials. In this Perspective, the current status of NN potentials is reviewed, and their advantages and limitations are discussed.

PMID:
21915403
DOI:
10.1039/c1cp21668f
[Indexed for MEDLINE]

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