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Aided by a neural network representation of the density functional theory(DFT) potential energy landscape of water in the RPBE approximation correctedfor dispersion, we calculate several structural and thermodynamic properties ofits liquid/vapor interface. The neural network speed allows us to bridge thesize and time scale gaps required to sample the properties of water along itsliquid/vapor coexistence line with unprecedented precision.

Ab-initio Structure and Thermodynamics of the RPBE-D3 Water/Vapor Interface by Neural-Network Molecular Dynamics