A Deep Potential model for liquid-vapor equilibrium and cavitation rates of water

Abstract

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here we utilize the Deep Potential methodology -- a machine learning approach -- to study this ubiquitous phase transition, starting from the phase diagram in the liquid-vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K, and evaluate the Deep Potential model performance against experimental results and the semi-empirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. We find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model, due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 $\pm$ 0.008) nm at 296.4 K. Lastly, we identify that water molecules display a preferential orientation in the liquid-vapor interface, in which H atoms tend to point towards the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behaviour is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid-vapor coexistence and water cavitation.

References in corpus (16)

• Canonical sampling through velocity-rescaling
• Ab initio theory and modeling of water
• On how good DFT exchange-correlation functionals are for H bonds in small water clusters: Benchmarks approaching the complete basis set limit
• Anomalies in water as obtained from computer simulations of the TIP4P/2005 model: density maxima, and density, isothermal compressibility and heat capacity minima
• The Madrid-2019 force field for electrolytes in water using TIP4P/2005 and scaled charges: extension to the ions F${}^{-}$, Br${}^{-}$, I${}^{-}$, Rb${}^{+}$, Cs${}^{+}$
• Homogeneous nucleation of ice
• Modification of the classical nucleation theory based on molecular simulation data for surface tension, critical nucleus size, and nucleation rate
• The Young-Laplace equation for a solid-liquid interface
• Direct Simulations of Homogeneous Bubble Nucleation: Agreement with CNT and no Local Hot Spots
• Seeding Approach to nucleation in the NVT ensemble: the case of bubble cavitation in overstretched Lennard Jones fluids
• Interfacial free energy and Tolman length of curved liquid-solid interfaces from equilibrium studies
• Homogeneous Ice Nucleation Rate in Water Droplets
• Bubble Evolution and Properties in Homogeneous Nucleation Simulations
• Phase equilibrium of liquid water and hexagonal ice from enhanced sampling molecular dynamics simulations
• Equivalence between condensation and boiling in a Lennard Jones fluid
• Extending and validating bubble nucleation rate predictions in a Lennard-Jones fluid with enhanced sampling methods and transition state theory

Cited by in corpus (2)

• Water at negative pressure: Nuclear quantum effects
• A unified machine-learning framework for ab initio multiscale modeling of liquids