Local structure analysis in ab initio liquid water

Abstract

Within the framework of density functional theory, the inclusion of exact exchange and non-local van der Waals/dispersion (vdW) interactions is crucial for predicting a microscopic structure of ambient liquid water that quantitatively agrees with experiment. In this work, we have used the local structure index (LSI) order parameter to analyse the local structure in such highly accurate ab initio liquid water. At ambient conditions, the LSI probability distribution, P(I ), was unimodal with most water molecules characterised by more disordered high-density-like local environments. With thermal excitations removed, the resultant bimodal P(I ) in the inherent potential energy surface (IPES) exhibited a 3:1 ratio between high-density- and low-density-like molecules, with the latter forming small connected clusters amid the predominant population. By considering the spatial correlations and hydrogen bond network topologies among water molecules with the same LSI identities, we demonstrate that the signatures of the experimentally observed low- and high-density amorphous phases of ice are present in the IPES of ambient liquid water. Analysis of the LSI autocorrelation function uncovered a persistence time of ∼ 4 ps – a finding consistent with the fact that natural thermal fluctuations are responsible for transitions between these distinct yet transient local aqueous environments in ambient liquid water.

Keywords:

• Ab initio molecular dynamics
• liquid water
• amorphous ice
• van der Waals
• hydrogen bond
• local structure index

Acknowledgements

All authors acknowledge support from the Scientific Discovery through Advanced Computing (SciDAC) program through the Department of Energy (DOE) under Grant No. DE-SC0008626. We also would like to acknowledge the DOE Computational Materials and Chemical Sciences Network (CMSCN) award DE-SC0005180, which provided support during the early stages of this work. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Scientific Discovery through Advanced Computing (SciDAC) program; the Department of Energy (DOE) [grant number DE-SC0008626]; the DOE Computational Materials and Chemical Sciences Network (CMSCN) award DE-SC0005180.