Telling hydrogen isotopologues in bulk water apart using path integral molecular dynamics and machine learning potentials

Thomsen, B.  ; Shiga, Motoyuki   

Water is a liquid which structure and properties are highly influenced by nuclear quantum effects (NQEs), as evidenced by the observed differences between light (HO) and heavy (DO) water. These differences are in theory revealed by conducting so called path integral molecular dynamics (PIMD) simulations. In the past we have used ab initio DFT based potentials to conduct such studies, which come with a large computational cost. We will here use machine learned potentials (MLPs) in place of DFT in order to reduce the computational cost and allow longer and more converged simulations of light and heavy water.