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Citation: L.C. Erhard, J. Rohrer, K. Albe, and V.L. Deringer (2022), "A machine-learned interatomic potential for silica and its relation to empirical models", npj Computational Materials, 8(1), 90. DOI: 10.1038/s41524-022-00768-w.
Abstract: Silica (SiO2) is an abundant material with a wide range of applications. Despite much progress, the atomistic modelling of the different forms of silica has remained a challenge. Here we show that by combining density-functional theory at the SCAN functional level with machine-learning-based interatomic potential fitting, a range of condensed phases of silica can be accurately described. We present a Gaussian approximation potential model that achieves high accuracy for the thermodynamic properties of the crystalline phases, and we compare its performance (and performance-cost trade-off) with that of multiple empirically fitted interatomic potentials for silica. We also include amorphous phases, assessing the ability of the potentials to describe structures of melt-quenched glassy silica, their energetic stability, and the high-pressure structural transition to a mainly sixfold-coordinated phase. We suggest that rather than standing on their own, machine-learned potentials for silica may be used in conjunction with suitable empirical models, each having a distinct role and complementing the other, by combining the advantages of the long simulation times afforded by empirical potentials and the near-quantum-mechanical accuracy of machine-learned potentials. This way, our work is expected to advance atomistic simulations of this key material and to benefit further computational studies in the field.

Notes: The potential was designed for crystalline, amorphous and liquid silica and shows also good behavior for certain high-pressure phases. It is not tested for silica surfaces and non stoichiometric phases (non SiO2).

See Computed Properties
Notes: These files were provided by Linus Erhard on Nov 1, 2022, and are alternatively available at the links listed below. For running the potential the QUIP package within LAMMPS is necessary. The file pot.in gives an example of the LAMMPS inputs to use to run this potential. Alternatively, the potential can be used in a python-ase interface called quippy.
File(s): Link(s):
zenodo, includes training data https://doi.org/10.5281/zenodo.6353684

Date Created: October 5, 2010 | Last updated: November 08, 2022