| Téléchargement | - Voir la version finale : AI-enhanced quantum simulations of oxocarbons using random sampling statistics under mechanical compression (PDF, 1.5 Mio)
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| DOI | Trouver le DOI : https://doi.org/10.1063/12.0028594 |
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| Auteur | Rechercher : Hu, Hang1; Rechercher : Ooi, Hsu Kiang (James); Rechercher : Hu, Anguang |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 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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| Sujet | machine learning; materials properties |
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| Résumé | 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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| Date de publication | 2024-12-09 |
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| Maison d’édition | AIP Publishing |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | e0a6a2dd-9bb3-41d3-83ec-a8eced9753a7 |
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| Enregistrement créé | 2024-12-20 |
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| Enregistrement modifié | 2024-12-23 |
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