Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Structure Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy

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 and provide a data-efficient approach to obtain machine-learned
condensed-phase potential energy surfaces using AFQMC, CCSD, and CCSD(T)
from a very small number (≤200) of energies by leveraging a
transfer learning scheme starting from lower-tier electronic structure
methods. We demonstrate the effectiveness of this approach for liquid
water by performing both classical and path integral molecular dynamics
simulations on these machine-learned potential energy surfaces. By
doing this, 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.