  Experiment Directed Simulations (EDS) is a method within a class of
techniques seeking to improve molecular simulations by minimally biasing the
system Hamiltonian to reproduce certain experimental observables. In a previous
application of EDS to ab initio molecular dynamics (AIMD) simulation-based on
electronic density functional theory (DFT), the AIMD simulations of water were
biased to reproduce its experimentally derived solvation structure. In
particular, by solely biasing the O-O pair correlation functions, other
structural and dynamical properties that were not biased were improved. In this
work, the hypothesis is tested that directly biasing the OH pair correlation,
will provide an even better improvement of DFT-based water properties in AIMD
simulations. The logic behind this hypothesis is that for most electronic DFT
descriptions of water the hydrogen bonding is known to be deficient due to
anomalous charge transfer and over polarization in the DFT. Using recent
advances to the EDS learning algorithm, we thus train a minimal bias on AIMD
water that reproduces the O-H radial distribution function derived from the
highly accurate MB-pol model of water. It is then confirmed that biasing the
O-H pair correlation alone can lead to improved AIMD water properties, with
structural and dynamical properties in even closer to experiment than the
previous EDS-AIMD model.
