× Warning! Note that elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimized separately. As with all interatomic potentials, please check to make sure that the performance is adequate for your problem.

× Updated! Potentials that share interactions are now listed as related models.

2024--Erhard-L-C-Rohrer-J-Albe-K-Deringer-V-L--Si-O

Citation: L.C. Erhard, J. Rohrer, K. Albe, and V.L. Deringer (2024), "Modelling atomic and nanoscale structure in the silicon–oxygen system through active machine learning", Nature Communications, 15(1), 1927. DOI: 10.1038/s41467-024-45840-9.

Abstract: Silicon-oxygen compounds are among the most important ones in the natural sciences, occurring as building blocks in minerals and being used in semiconductors and catalysis. Beyond the well-known silicon dioxide, there are phases with different stoichiometric composition and nanostructured composites. One of the key challenges in understanding the Si-O system is therefore to accurately account for its nanoscale heterogeneity beyond the length scale of individual atoms. Here we show that a unified computational description of the full Si-O system is indeed possible, based on atomistic machine learning coupled to an active-learning workflow. We showcase applications to very-high-pressure silica, to surfaces and aerogels, and to the structure of amorphous silicon monoxide. In a wider context, our work illustrates how structural complexity in functional materials beyond the atomic and few-nanometre length scales can be captured with active machine learning.

Notes: The potential is well suited for Si, SiO₂ and mixtures of both under ambient conditions (crystalline as well as amorphous). Moreover, it is trained for surfaces of SiO₂ and all high-pressure phases of SiO₂ including the amorphous phase (at least up to 200 GPa). It can be also used for Si surfaces. It should not be used for high-pressure Si and mixtures of Si-SiO₂ under high pressures.

LAMMPS pair_style pace (2024--Erhard-L-C--Si-O--LAMMPS--ipr1)

See Computed Properties
Notes: This file was provided by Linus Erhard on March 6, 2024. The LAMMPS pace pair_style is available by building LAMMPS with the ML-PACE package, and can be ran with CPUs and GPUs. The Zenodo link contains additional files, such as training data, parameter files, example scripts and simulation results.
File(s):

SiOx_potential.yace

Link(s):

Zenodo https://zenodo.org/records/10419194

ASE calculator (2024--Erhard-L-C--Si-O--ase--ipr1)

Notes: This file was provided by Linus Erhard on March 6, 2024. It can be used for an ASE calculator with the python-ace package https://pacemaker.readthedocs.io.
File(s):

SiOx_potential.yaml