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

Abstract

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

published in J. Chem. Phys.; accepted version ; 6 pages, 6 figures

References in corpus (3)

• Machine Learning Unifies the Modelling of Materials and Molecules
• How van der Waals interactions determine the unique properties of water
• Ab initio theory and modeling of water

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