Liquid-liquid transition and ice crystallization in a machine-learned coarse-grained water model.

ABSTRACT: Mounting experimental evidence supports the existence of a liquid-liquid transition (LLT) in high-pressure supercooled water. However, fast crystallization of supercooled water has impeded identification of the LLT line T_LL(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 Machine-Learned Bond-Order Potential (ML-BOP) has a LLT that ends in a critical point at p_c = 170 ± 10 MPa and T_c

SUBMITTER: Dhabal D 

PROVIDER: S-EPMC11098087 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature