| Download | - Will be available here on December 9, 2025
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| DOI | Resolve DOI: https://doi.org/10.1063/12.0028594 |
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| Author | Search for: Hu, Hang1; Search for: Ooi, Hsu Kiang (James); Search for: Hu, Anguang |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | The 23rd Biennial American Physical Society Conference on Shock Compression of Condensed Matter (SCCM-2023), June 19-23, 2003, Chicago, Illinois, United States |
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| Subject | machine learning; materials properties |
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| Abstract | This study employs a Δ-machine learning model to derive structure search parameters utilized in the AIRSS package for predicting stable high-density oxocarbon materials. These parameters, including minimum intermolecular distances and densities, guide the search from molecular precursors to transform into solid oxocarbon systems. These oxygenated carbon network solids exhibit stable triangular planar carbon networks. Furthermore, we examine the transformational bonding pathways of C₃O₂, C₅O₂, and C₇O₂ oxocarbon solids under mechanical compression, identifying three stages: van der Waals compression, bond-breaking and forming, and final relaxation. Our findings demonstrate the potential of stable high-density oxocarbon systems across diverse structures. Future research focusing on electronic and thermal properties will be pivotal in realizing their full potential and facilitating widespread adoption. |
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| Publication date | 2024-12-09 |
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| Publisher | AIP Publishing |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | e0a6a2dd-9bb3-41d3-83ec-a8eced9753a7 |
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| Record created | 2024-12-20 |
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| Record modified | 2024-12-23 |
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