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

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