期刊
JOURNAL OF CHEMICAL PHYSICS
卷 136, 期 6, 页码 -出版社
AIP Publishing
DOI: 10.1063/1.3682557
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资金
- Deutsche Forschungsgemeinschaft (DFG)
- Fonds der Chemischen Industrie
- Research Department Interfacial Systems Chemistry (IFSC) of the Ruhr-Universitat Bochum
Understanding the unique properties of water still represents a significant challenge for theory and experiment. Computer simulations by molecular dynamics require a reliable description of the atomic interactions, and in recent decades countless water potentials have been reported in the literature. Still, most of these potentials contain significant approximations, for instance a frozen internal structure of the individual water monomers. Artificial neural networks (NNs) offer a promising way for the construction of very accurate potential-energy surfaces taking all degrees of freedom explicitly into account. These potentials are based on electronic structure calculations for representative configurations, which are then interpolated to a continuous energy surface that can be evaluated many orders of magnitude faster. We present a full-dimensional NN potential for the water dimer as a first step towards the construction of a NN potential for liquid water. This many-body potential is based on environment-dependent atomic energy contributions, and long-range electrostatic interactions are incorporated employing environment-dependent atomic charges. We show that the potential and derived properties like vibrational frequencies are in excellent agreement with the underlying reference density-functional theory calculations. (C) 2012 American Institute of Physics. [doi:10.1063/1.3682557]
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