<i>Ab initio</i> structure and thermodynamics of the RPBE-D3 water/vapor interface by neural-network molecular dynamics
Oliver Wohlfahrt, Christoph Dellago, Marcello Sega
Abstract
Aided by a neural network representation of the density functional theory potential energy landscape of water in the Revised Perdew-Burke-Ernzerhof 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.
Topics & Concepts
Statistical physicsRepresentation (politics)Non-equilibrium thermodynamicsThermodynamicsMolecular dynamicsArtificial neural networkScale (ratio)PhysicsInterface (matter)Energy (signal processing)Density functional theoryMaterials scienceLine (geometry)Measure (data warehouse)Sample (material)Network structureThermodynamic potentialPotential energyComplex systemBridge (graph theory)Stability (learning theory)Current (fluid)ChemistrySimple (philosophy)Network theoryThermodynamic equilibriumMachine Learning in Materials ScienceSpectroscopy and Quantum Chemical StudiesPhase Equilibria and Thermodynamics