  Obtaining the atomistic structure and dynamics of disordered condensed phase
systems from first principles remains one of the forefront challenges of
chemical theory. Here we exploit recent advances in periodic electronic
structure to show that, by leveraging transfer learning starting from lower
tier electronic structure methods, one can obtain machine learned potential
energy surfaces for liquid water from the higher tier AFQMC, CCSD, and CCSD(T)
approaches using $\le$200 energies. By performing both classical and path
integral molecular dynamics simulations on these machine learned potential
energy surfaces we uncover the interplay of dynamical electron correlation and
nuclear quantum effects across the entire liquid range of water while providing
a general strategy for efficiently utilizing periodic correlated electronic
structure methods to explore disordered condensed phase systems.
