  Machine learning interatomic potentials (MLIPs) are an emerging modeling
technique that promises to provide electronic structure theory accuracy for a
fraction of its cost, however, the transferability of MLIPs is a largely
unknown factor. Recently, it has been proposed (J. Chem. Phys., 2023, 158,
084111) that MLIPs trained on solely liquid water data cannot describe
vapor-liquid equilibrium while recovering the many-body decomposition analysis
of gas-phase water clusters, as MLIPs do not directly learn the physically
correct interactions of water molecules, limiting transferability. In this
work, we show that MLIPs based on an equivariant neural network architecture
trained on only 3,200 bulk liquid water structures reproduces liquid-phase
water properties (e.g., density within 0.003 g/cm3 between 230 and 365 K),
vapor-liquid equilibrium properties up to 550 K, the many-body decomposition
analysis of gas-phase water cluster up to six-body interactions, and the
relative energy and the vibrational density of states of ice phases. This study
highlights that state-of-the-art MLIPs have the potential to develop
transferable models for arbitrary phases of water that remain stable in
nanosecond-long simulations.
