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

Machine learning potentials from transfer learning of periodic  correlated electronic structure methods: Application to liquid water with  AFQMC, CCSD, and CCSD(T)