Structure-property relations of silicon oxycarbides studied using a machine learning interatomic potential
arxiv(2024)
摘要
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 DFT 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.
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