Theoretical prediction of the homogeneous ice nucleation rate: disentangling thermodynamics and kinetics

TL;DR

This paper presents a theoretical calculation of the homogeneous ice nucleation rate, accounting for interface diffuseness, stacking disorders, and kinetic factors, revealing a rate 9 orders of magnitude faster than previous estimates.

Contribution

It systematically disentangles thermodynamic and kinetic contributions to improve the accuracy of ice nucleation rate predictions.

Findings

Predicted nucleation rate is 9 orders of magnitude faster than previous estimates.

Disentangling thermodynamic and kinetic factors clarifies sources of discrepancies.

Considering interface diffuseness and stacking disorders improves model accuracy.

Abstract

Estimating the homogeneous ice nucleation rate from undercooled liquid water is at the same time crucial for understanding many important physical phenomena and technological applications, and challenging for both experiments and theory. From a theoretical point of view, difficulties arise due to the long time scales required, as well as the numerous nucleation pathways involved to form ice nuclei with different stacking disorders. We computed the homogeneous ice nucleation rate at a physically relevant undercooling for a single-site water model, taking into account the diffuse nature of ice-water interfaces, stacking disorders in ice nuclei, and the addition rate of particles to the critical nucleus.We disentangled and investigated the relative importance of all the terms, including interfacial free energy, entropic contributions and the kinetic prefactor, that contribute to the overall nucleation rate.There has been a long-standing discrepancy for the predicted homogeneous ice nucleation rates, and our estimate is faster by 9 orders of magnitude compared with previous literature values. Breaking down the problem into segments and considering each term carefully can help us understand where the discrepancy may come from and how to systematically improve the existing computational methods.