Data for: A Computationally Efficient Quasi-Harmonic Study of Ice Polymorphs Using the FFLUX Force Field
Description
This repository provides additional data to accompany the paper: "A Computationally Efficient Quasi-Harmonic Study of Ice Polymorphs Using the FFLUX Force Field" A. Pák, M. L. Brown and P. L. A. Popelier Acta Crystallographica A (2025) DOI: https://doi.org/10.1107/S2053273324010921. In this article the machine learning force field FFLUX is applied to ice polymorphs in geometry optimisations, calculation of phonon spectra, and free energies. In addition to Helmholtz free energies, Gibbs free energies were calculated for the first time using FFLUX under the quasi-harmonic approximation. Data from these calculations is available in this repository, including: • The Gaussian process regression machine learning model used in the FFLUX calculations; • Files used to test the electrostatic energy prediction of the model; • Files used to generate the optimised structures; • Files used to calculate the phonon density of states, phonon dispersions and Helmholtz free energies; • Files used to calculate the Gibbs free energies, with data at each compressed and expanded volume. Details are provided for how the data was generated in the published paper and the supporting information. Input files for the Vienna Ab Initio Simulation Package (VASP) code are also included in the repository. Although FFLUX has not yet been made publicly available, the water Gaussian process regression models and input files are given for when it is made available.