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

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 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

SUBMITTER: Piaggi PM 

PROVIDER: S-EPMC9388152 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature