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Structure–property relations of silicon oxycarbides studied using a machine learning interatomic potential

Leimeroth, Niklas ; Rohrer, Jochen ; Albe, Karsten (2024)
Structure–property relations of silicon oxycarbides studied using a machine learning interatomic potential.
In: Journal of the American Ceramic Society, 2024, 107 (10)
doi: 10.26083/tuprints-00028275
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Structure–property relations of silicon oxycarbides studied using a machine learning interatomic potential
Language: English
Date: 19 November 2024
Place of Publication: Darmstadt
Year of primary publication: October 2024
Place of primary publication: Oxford
Publisher: Wiley-Blackwell
Journal or Publication Title: Journal of the American Ceramic Society
Volume of the journal: 107
Issue Number: 10
DOI: 10.26083/tuprints-00028275
Corresponding Links:
Origin: Secondary publication DeepGreen
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.

Uncontrolled Keywords: atomistic simulation, silicon oxycarbide, structure, Young‐s modulus
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-282758
Additional Information:

This article also appears in: Editor’s Choice JACerS 2024

Classification DDC: 500 Science and mathematics > 540 Chemistry
600 Technology, medicine, applied sciences > 660 Chemical engineering
Divisions: 11 Department of Materials and Earth Sciences > Material Science > Materials Modelling
Date Deposited: 19 Nov 2024 12:28
Last Modified: 05 Dec 2024 12:04
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/28275
PPN: 524314578
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