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 |
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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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