Data from "Homogeneous ice nucleation in an ab initio machine learning model"

This dataset contains input and output files to reproduce the results of
the manuscript "Homogeneous ice nucleation in an ab initio machine
learning model" by Pablo M. Piaggi, Jack Weis, Athanassios Z.
Panagiotopoulos, Pablo G. Debenedetti, and Roberto Car (arXiv preprint
https://arxiv.org/abs/2203.01376). In this work, we studied the
homogeneous nucleation of ice from supercooled liquid water using a
machine learning model trained on ab initio energies and forces. Since
nucleation takes place over times much longer than the simulation times
that can be afforded using molecular dynamics simulations, we make use of
the seeding technique that is based on simulating an ice cluster embedded
in liquid water. 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 shrinking at the given supersaturation). Using
data from the seeding simulations and the equations of classical
nucleation theory we compute nucleation rates that can be compared with
experiments.