Accurate prediction of heat conductivity of water by a neuroevolution potential

Abstract

We propose an approach that can accurately predict the heat conductivity of liquid water. On the one hand, we develop an accurate machine-learned potential based on the neuroevolution-potential approach that can achieve quantum-mechanical accuracy at the cost of empirical force fields. On the other hand, we combine the Green-Kubo method and the spectral decomposition method within the homogeneous nonequilibrium molecular dynamics framework to account for the quantum-statistical effects of high-frequency vibrations. Excellent agreement with experiments under both isobaric and isochoric conditions within a wide range of temperatures is achieved using our approach.

8 pages, 7 figures

References in corpus (12)

• Canonical sampling through velocity-rescaling
• Pressure control using stochastic cell rescaling
• How van der Waals interactions determine the unique properties of water
• GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
• The Phase Diagram of a Deep Potential Water Model
• Improving the accuracy of the neuroevolution machine learning potential for multi-component systems
• Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine-learning molecular dynamics simulations
• Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullerene
• Heat transport in liquid water from first-principles and deep-neural-network simulations
• Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn${}_x$Ge${}_y$ compounds
• Variable thermal transport in black, blue, and violet phosphorene from extensive atomistic simulations with a neuroevolution potential
• Nuclear quantum effects in thermal conductivity from centroid molecular dynamics

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• Advances in modeling complex materials: The rise of neuroevolution potentials
• Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials
• Insight into the effect of force error on the thermal conductivity from machine-learned potentials
• PYSED: A tool for extracting kinetic-energy-weighted phonon dispersion and lifetime from molecular dynamics simulations
• Combining linear-scaling quantum transport and machine-learning molecular dynamics to study thermal and electronic transports in complex materials
• Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential
• NEP-MB-pol: A unified machine-learned framework for fast and accurate prediction of water's thermodynamic and transport properties
• Probing the ideal limit of interfacial thermal conductance in two-dimensional van der Waals heterostructures
• Phonon coherence and minimum thermal conductivity in disordered superlattice
• Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water
• Thermal conductivity of Li${}_3$PS${}_4$ solid electrolytes with ab initio accuracy
• Strain-Gradient-Driven Decoupling of Thermal Suppression from Anisotropy in \b{eta}-Ga2O3
• Role of octahedral tilting induced acoustic softening on limiting thermal transport in SrSnO3
• Thermal conductivities of monolayer graphene oxide from machine learning molecular dynamics simulations
• Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids