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.
Citation: N. Leimeroth, J. Rohrer, and K. Albe (2024), "Structure–property relations of silicon oxycarbides studied using a machine learning interatomic potential", Journal of the American Ceramic Society, 107(10), 6896–6910. DOI: 10.1111/jace.19932.
Abstract: Silicon oxycarbides show outstanding versatility due to their highly tunable composition and microstructure. Consequently, a key challenge is a thorough knowledge of structure–property relations in the system. In this work, we fit an atomic cluster expansion potential to a set of actively learned density-functional theory training data spanning a wide configurational space. We demonstrate the ability of the potential to produce realistic amorphous structures and rationalize the formation of different morphologies of the turbostratic free carbon phase. Finally, we relate the materials stiffness to its composition and microstructure, finding a delicate dependence on Si-C bonds that contradicts commonly assumed relations to the free carbon phase.
Notes: This potential was designed to model model glass-ceramics. It is not thoroughly tested for pure Si, C or Si-C phases, but should still do reasonably well due the employed active learning strategy in the fitting process.