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

Bohayra Mortazavi, Fazel Shojaei, Alexander V. Shapeev, Xiaoying Zhuang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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 C6N7 with a nanoporous structure has been fabricated. Based on the experimentally realized C6N7 lattice and by altering the linkage chemistry, we introduce three novel carbon nitride lattices of C6N7–C2, C6N7-BN and C6N7–C2H2. Density functional theory (DFT) simulations are next utilized in order to investigate energetic stability, electronic, mechanical response, and optical characteristics of novel C6N7-based monolayers. The dynamical stability and mechanical properties are explored using machine-learning interatomic potentials (MLIPs). The presented results confirm that C6N7-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 C6N7-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.

Original languageEnglish
Pages (from-to)230-239
Number of pages10
JournalCarbon
Volume194
DOIs
Publication statusPublished - Jul 2022

Keywords

  • 2D Carbon nitride
  • Machine-learning
  • Mechanical
  • Nanoporous
  • Semiconductors

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