Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional

TL;DR

This study combines machine learning and density functional theory to accurately investigate phase equilibrium properties of water and ice polymorphs, providing insights into ice nucleation and stability with improved predictive accuracy.

Contribution

It introduces a novel approach using a deep neural network trained on SCAN DFT data to compute complex phase properties of ice, surpassing semiempirical potentials in accuracy.

Findings

Correct qualitative prediction of melting temperatures and stability.

Quantitative agreement with experimental data for key properties.

SCN predicts ice Ih as more stable than ice Ic.

Abstract

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye towards studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ice Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic.