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Title: A Deep Potential model for liquid–vapor equilibrium and cavitation rates of water

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/5.0144500· OSTI ID:1992043

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 semiempirical 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. Further, 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 ± 0.008) nm at 296.4 K. Finally, we identify that water molecules display a preferential orientation in the liquid–vapor interface, in which H atoms tend to point toward the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behavior 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.

Research Organization:
Princeton Univ., NJ (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Basic Energy Sciences (BES); Engineering & Physical Sciences Research Council (EPSRC); Roger Ekins Research Fellowship; Oppenheimer Research Fellowship; Ramon y Cajal Fellowship
Grant/Contract Number:
SC0002128; EP/T517847/1; RYC2021-030937-I; SC001934; SC0019394
OSTI ID:
1992043
Alternate ID(s):
OSTI ID: 1994524
Journal Information:
Journal of Chemical Physics, Vol. 158, Issue 18; ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)Copyright Statement
Country of Publication:
United States
Language:
English

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