# Accurate prediction of heat conductivity of water by a neuroevolution potential

## TL;DR

This paper introduces a machine learning-based potential combined with advanced simulation techniques to accurately predict water's heat conductivity, aligning well with experimental data across various conditions.

## Contribution

It develops a neuroevolution-based potential and integrates Green-Kubo and spectral methods to incorporate quantum effects in heat conductivity predictions.

## Key findings

- Achieves quantum-mechanical accuracy in heat conductivity predictions.
- Matches experimental results across temperature and pressure conditions.
- Provides a computationally efficient alternative to quantum simulations.

## 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.