Ab-initio Structure and Thermodynamics of the RPBE-D3 Water/Vapor Interface by Neural-Network Molecular Dynamics
- Author(s)
- 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.
- Organisation(s)
- Computational and Soft Matter Physics
- External organisation(s)
- Forschungszentrum Jülich
- Journal
- Journal of Chemical Physics
- Volume
- 153
- No. of pages
- 6
- ISSN
- 0021-9606
- DOI
- https://doi.org/10.1063/5.0021852
- Publication date
- 2020
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103015 Condensed matter, 103043 Computational physics, 103006 Chemical physics, 103029 Statistical physics
- Keywords
- ASJC Scopus subject areas
- General Physics and Astronomy, Physical and Theoretical Chemistry
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/b190e6b0-9ac3-4586-a91b-86f7c750ecaa