A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Autoren

  • Bohayra Mortazavi
  • Fazel Shojaei
  • Alexander V Shapeev
  • Xiaoying Zhuang

Organisationseinheiten

Externe Organisationen

  • Persian Gulf University
  • Skolkovo Innovation Center
  • Tongji University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)230-239
Seitenumfang10
FachzeitschriftCARBON
Jahrgang194
Frühes Online-Datum31 März 2022
PublikationsstatusVeröffentlicht - Juli 2022

Abstract

Carbon nitride nanoporous lattices are nowadays among the most appealing two-dimensional (2D) nanomaterials for diverse cutting-edge technologies. In one of the recent advances, novel C–C bridged heptazine of C 6N 7 with a nanoporous structure has been fabricated. Based on the experimentally realized C 6N 7 lattice and by altering the linkage chemistry, we introduce three novel carbon nitride lattices of C 6N 7–C 2, C 6N 7-BN and C 6N 7–C 2H 2. Density functional theory (DFT) simulations are next utilized in order to investigate energetic stability, electronic, mechanical response, and optical characteristics of novel C 6N 7-based monolayers. The dynamical stability and mechanical properties are explored using machine-learning interatomic potentials (MLIPs). The presented results confirm that C 6N 7-based monolayers are stable and strong semiconductors with notable absorption of the ultraviolet range of light. Remarkable accuracy of the developed computationally-efficient classical models is confirmed by comparing the predictions with those by DFT. Findings by the combined DFT and MLIP methods confirm the stability of novel C 6N 7-based nanosheets and provide a comprehensive vision on their highly appealing physical properties. More importantly, this study confirms the outstanding robustness and efficiency of MLIPs in substituting the computationally expensive DFT methods in the exploration of complex phononic and mechanical/failure responses of low-symmetry and highly-porous conductive frameworks.

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A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides. / Mortazavi, Bohayra; Shojaei, Fazel; Shapeev, Alexander V et al.
in: CARBON, Jahrgang 194, 07.2022, S. 230-239.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Mortazavi B, Shojaei F, Shapeev AV, Zhuang X. A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides. CARBON. 2022 Jul;194:230-239. Epub 2022 Mär 31. doi: 10.1016/j.carbon.2022.03.068
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title = "A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides",
abstract = "Carbon nitride nanoporous lattices are nowadays among the most appealing two-dimensional (2D) nanomaterials for diverse cutting-edge technologies. In one of the recent advances, novel C–C bridged heptazine of C 6N 7 with a nanoporous structure has been fabricated. Based on the experimentally realized C 6N 7 lattice and by altering the linkage chemistry, we introduce three novel carbon nitride lattices of C 6N 7–C 2, C 6N 7-BN and C 6N 7–C 2H 2. Density functional theory (DFT) simulations are next utilized in order to investigate energetic stability, electronic, mechanical response, and optical characteristics of novel C 6N 7-based monolayers. The dynamical stability and mechanical properties are explored using machine-learning interatomic potentials (MLIPs). The presented results confirm that C 6N 7-based monolayers are stable and strong semiconductors with notable absorption of the ultraviolet range of light. Remarkable accuracy of the developed computationally-efficient classical models is confirmed by comparing the predictions with those by DFT. Findings by the combined DFT and MLIP methods confirm the stability of novel C 6N 7-based nanosheets and provide a comprehensive vision on their highly appealing physical properties. More importantly, this study confirms the outstanding robustness and efficiency of MLIPs in substituting the computationally expensive DFT methods in the exploration of complex phononic and mechanical/failure responses of low-symmetry and highly-porous conductive frameworks. ",
keywords = "2D Carbon nitride, Machine-learning, Mechanical, Nanoporous, Semiconductors",
author = "Bohayra Mortazavi and Fazel Shojaei and Shapeev, {Alexander V} and Xiaoying Zhuang",
note = "Funding Information: B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). A.V.S. is supported by the Russian Science Foundation (Grant No 18-13-00479, https://rscf.ru/project/18-13-00479/). F.S. thanks the Persian Gulf University Research Council for support of this study. The authors are greatly thankful to the VEGAS cluster at the Bauhaus University of Weimar for providing the computational resources. ",
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volume = "194",
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Download

TY - JOUR

T1 - A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides

AU - Mortazavi, Bohayra

AU - Shojaei, Fazel

AU - Shapeev, Alexander V

AU - Zhuang, Xiaoying

N1 - Funding Information: B.M. and X.Z. appreciate the funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122, Project ID 390833453). A.V.S. is supported by the Russian Science Foundation (Grant No 18-13-00479, https://rscf.ru/project/18-13-00479/). F.S. thanks the Persian Gulf University Research Council for support of this study. The authors are greatly thankful to the VEGAS cluster at the Bauhaus University of Weimar for providing the computational resources.

PY - 2022/7

Y1 - 2022/7

N2 - Carbon nitride nanoporous lattices are nowadays among the most appealing two-dimensional (2D) nanomaterials for diverse cutting-edge technologies. In one of the recent advances, novel C–C bridged heptazine of C 6N 7 with a nanoporous structure has been fabricated. Based on the experimentally realized C 6N 7 lattice and by altering the linkage chemistry, we introduce three novel carbon nitride lattices of C 6N 7–C 2, C 6N 7-BN and C 6N 7–C 2H 2. Density functional theory (DFT) simulations are next utilized in order to investigate energetic stability, electronic, mechanical response, and optical characteristics of novel C 6N 7-based monolayers. The dynamical stability and mechanical properties are explored using machine-learning interatomic potentials (MLIPs). The presented results confirm that C 6N 7-based monolayers are stable and strong semiconductors with notable absorption of the ultraviolet range of light. Remarkable accuracy of the developed computationally-efficient classical models is confirmed by comparing the predictions with those by DFT. Findings by the combined DFT and MLIP methods confirm the stability of novel C 6N 7-based nanosheets and provide a comprehensive vision on their highly appealing physical properties. More importantly, this study confirms the outstanding robustness and efficiency of MLIPs in substituting the computationally expensive DFT methods in the exploration of complex phononic and mechanical/failure responses of low-symmetry and highly-porous conductive frameworks.

AB - Carbon nitride nanoporous lattices are nowadays among the most appealing two-dimensional (2D) nanomaterials for diverse cutting-edge technologies. In one of the recent advances, novel C–C bridged heptazine of C 6N 7 with a nanoporous structure has been fabricated. Based on the experimentally realized C 6N 7 lattice and by altering the linkage chemistry, we introduce three novel carbon nitride lattices of C 6N 7–C 2, C 6N 7-BN and C 6N 7–C 2H 2. Density functional theory (DFT) simulations are next utilized in order to investigate energetic stability, electronic, mechanical response, and optical characteristics of novel C 6N 7-based monolayers. The dynamical stability and mechanical properties are explored using machine-learning interatomic potentials (MLIPs). The presented results confirm that C 6N 7-based monolayers are stable and strong semiconductors with notable absorption of the ultraviolet range of light. Remarkable accuracy of the developed computationally-efficient classical models is confirmed by comparing the predictions with those by DFT. Findings by the combined DFT and MLIP methods confirm the stability of novel C 6N 7-based nanosheets and provide a comprehensive vision on their highly appealing physical properties. More importantly, this study confirms the outstanding robustness and efficiency of MLIPs in substituting the computationally expensive DFT methods in the exploration of complex phononic and mechanical/failure responses of low-symmetry and highly-porous conductive frameworks.

KW - 2D Carbon nitride

KW - Machine-learning

KW - Mechanical

KW - Nanoporous

KW - Semiconductors

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VL - 194

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EP - 239

JO - CARBON

JF - CARBON

SN - 0008-6223

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