Homogeneous ice nucleation in an ab initio machine learning model of water

TL;DR

This study uses an efficient machine learning model trained on DFT data to simulate homogeneous ice nucleation at an ab initio level, achieving results consistent with experiments and exploring key factors influencing nucleation rates.

Contribution

It introduces a computationally feasible ab initio-level simulation of ice nucleation using machine learning, enabling large-scale studies of nucleation mechanisms.

Findings

Nucleation rates agree with experimental data at moderate supercoolings.

Critical cluster size can be accurately determined using the seeding technique.

Properties like thermodynamic driving force and interfacial free energy significantly impact nucleation rates.

Abstract

Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine learning model trained on density-functional theory (DFT) energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation) which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.