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dc.contributorWeis, Jack-
dc.contributorPanagiotopoulos, Athanassios-
dc.contributorDebenedetti, Pablo-
dc.contributorCar, Roberto-
dc.contributor.authorPiaggi, Pablo-
dc.coverage.spatialPrinceton, New Jersey, USAen_US
dc.coverage.temporalstart=2021:01-01 ; end=2022:04-01en_US
dc.descriptionThis dataset is too large to download directly from this item page. You can access and download the data via Globus-
dc.description.abstractThis 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 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.en_US
dc.publisherPrinceton Universityen_US
dc.rightsCC BY 4.0 Internationalen_US
dc.subjectmolecular simulationen_US
dc.subjectmachine learningen_US
dc.subjectab initioen_US
dc.titleData from "Homogeneous ice nucleation in an ab initio machine learning model"en_US
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