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 m...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.展开更多
基金supported by the National Science and Technology Advanced Materials Major Program of China(No.2024ZD0606900)KX,TL,and JX acknowledge support from the National Key R&D Project from the Ministry of Science and Technology of China(No.2022YFA1203100)+1 种基金the Research Grants Council of Hong Kong(No.AoE/P-701/20)RGC GRF(No.14220022).
文摘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.
