Liquid-Liquid Transition in a Machine-Learned Coarse grained Water Model

Mounting experimental evidence supports the existence of a liquid-liquid transition (LLT) in high-pressure supercooled water. However, fast crystallization of supercooled water impedes the identification of the LLT line TLL(p) in experiments. While the most accurate all-atom (AA) water models display a LLT, their computational cost limits investigations of its interplay with ice formation. Coarse-grained (CG) models provide over 100-fold computational efficiency gain over AA models, enabling the study of water crystallization, but have not yet shown to have a LLT. Here we demonstrate that the CG machine-learned water model ML-BOP has a LLT that ends in a criti-cal point at pc = 170±10 MPa and Tc = 181±3 K. The TLL(p) of ML-BOP is almost identical to the one of TIP4P/2005, adding to the similarity in the equation of state of liquid water in both models. The simulations reveal that TLL(p) coincides with the line of maximum crystallization rate Tx(p) of supercooled ML-BOP, supporting a mechanistic relationship between the structural transformation in liquid water and the formation of ice.