NEP-MB-pol: a unified machine-learned framework for fast and accurate prediction of water’s thermodynamic and transport properties

Abstract

The complex interatomic interactions and strong nuclear quantum effects in water pose significant challenges for accurately modeling its structural, thermodynamic, and transport behavior across varied conditions. While machine-learned potentials have improved the prediction of either static or transport properties individually, a unified computational framework that accurately captures both has remained elusive. Here, we introduce a machine-learned framework with a highly accurate and efficient neuroevolution potential trained on extensive many-body polarization reference data approaching coupled-cluster-level accuracy, combined with path-integral molecular dynamics and quantum-correction techniques. By capturing the quantum nature of water, this framework accurately predicts its structural, thermodynamic, and transport properties across a broad temperature range, enabling fast, accurate, and simultaneous prediction of self-diffusion coefficient, viscosity, and thermal conductivity. This work represents a major stride in water modeling, providing a unified and robust approach for exploring water’s thermodynamic and transport properties, with broad applications across multiple scientific disciplines.