CGNEP‐MB‐pol: A Single‐Site Coarse‐Grained Machine Learning Potential for Water
TL;DR
A CG machine learning potential for water, named CGNEP‐MB‐pol, is introduced, which integrates a one‐molecule to one‐bead mapping with the neuroevolution potential (NEP) framework and MB‐pol reference data, thereby aiming to alleviate, within the liquid‐water and ice‐Ih states examined here, the state dependence and limited transferability that often constrain conventional CG models.
Abstract
Coarse‐grained (CG) molecular dynamics (MD) can greatly extend the accessible time and length scales for water, provided that the reduced model captures key structural, dynamical, and thermodynamic properties. Here, we introduce a CG machine learning potential (MLP) for water, named CGNEP‐MB‐pol, which integrates a one‐molecule to one‐bead mapping with the neuroevolution potential (NEP) framework and MB‐pol reference data, thereby aiming to alleviate, within the liquid‐water and ice‐Ih states examined here, the state dependence and limited transferability that often constrain conventional CG models. Without altering the NEP descriptor or network architecture, the model is trained on CG coordinates and labels derived from atomistic configurations through a two‐step dataset refinement strategy, combining multi‐state training based on forces, energies, and virials. CGNEP‐MB‐pol remains compatible with the GPUMD + NEP workflow while delivering a substantial speedup over its all‐atom counterpart. It reproduces mapped reference forces, radial distribution functions of liquid water and ice, and the overall temperature dependence of self‐diffusion and viscosity. Two‐phase simulations capture ice growth, melting, and solid–liquid coexistence, yielding a melting temperature of approximately 274 K. This study establishes a practical route for constructing CG‐MLPs from high‐fidelity atomistic reference models.