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• W2133959849 startingPage "1191" @default.
• W2133959849 abstract "The development of reliable interatomic potentials for large-scale molecular dynamics (MD) simulations of chemical processes at surfaces and interfaces is a formidable challenge because a wide range of atomic environments and very different types of bonding can be present. In recent years interatomic potentials based on artificial neural networks (NNs) have emerged offering an unbiased approach to the construction of potential energy surfaces (PESs) for systems that are difficult to describe by conventional potentials. Here, we review the basic properties of NN potentials and describe their construction for materials like metals and oxides. The accuracy and efficiency are demonstrated using copper and zinc oxide as benchmark systems. First results for a potential of the combined ternary CuZnO system aiming at the description of oxide-supported copper clusters are reported. Model of a copper cluster at the ZnO() surface." @default.
• W2133959849 created "2016-06-24" @default.
• W2133959849 creator A5026774143 @default.
• W2133959849 creator A5040792944 @default.
• W2133959849 creator A5051210782 @default.
• W2133959849 date "2012-11-29" @default.
• W2133959849 modified "2023-10-18" @default.
• W2133959849 title "Neural network potentials for metals and oxides - First applications to copper clusters at zinc oxide" @default.
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• W2133959849 doi "https://doi.org/10.1002/pssb.201248370" @default.
• W2133959849 hasPublicationYear "2012" @default.
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