**Isotope effects in liquid water via deep potential molecular dynamics**

HY Ko and LF Zhang and B Santra and H Wang and WN E and RA DiStasio and R Car, MOLECULAR PHYSICS, 117, 3269-3281 (2019).

DOI: 10.1080/00268976.2019.1652366

A comprehensive microscopic understanding of ambient liquid water is a
major challenge for ab initio simulations as it simultaneously requires
an accurate quantum mechanical description of the underlying potential
energy surface (PES) as well as extensive sampling of configuration
space. Due to the presence of light atoms (e.g. or ), nuclear quantum
fluctuations lead to observable changes in the structural properties of
liquid water (e.g. isotope effects), and therefore provide yet another
challenge for ab initio approaches. In this work, we demonstrate that
the combination of dispersion-inclusive hybrid density functional theory
(DFT), the Feynman discretised path-integral (PI) approach, and machine
learning (ML) constitutes a versatile ab initio based framework that
enables extensive sampling of both thermal and nuclear quantum
fluctuations on a quite accurate underlying PES. In particular, we
employ the recently developed deep potential molecular dynamics (DPMD)
model ? a neural-network representation of the ab initio PES ? in
conjunction with a PI approach based on the generalised Langevin
equation (PIGLET) to investigate how isotope effects influence the
structural properties of ambient liquid and . Through a detailed
analysis of the interference differential cross sections as well as
several radial and angular distribution functions, we demonstrate that
this approach can furnish a semi-quantitative prediction of these subtle
isotope effects. **GRAPHICS** .

Return to Publications page