  We propose an approach that can accurately predict the heat conductivity of
liquid water. On the one hand, we develop an accurate machine-learned potential
based on the neuroevolution-potential approach that can achieve
quantum-mechanical accuracy at the cost of empirical force fields. On the other
hand, we combine the Green-Kubo method and the spectral decomposition method
within the homogeneous nonequilibrium molecular dynamics framework to account
for the quantum-statistical effects of high-frequency vibrations. Excellent
agreement with experiments under both isobaric and isochoric conditions within
a wide range of temperatures is achieved using our approach.
