A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous CN monolayers.

Carbon nitride nanomembranes are currently among the most appealing two-dimensional (2D) materials. As a nonstop endeavor in this field, a novel 2D fused aromatic nanoporous network with a CN stoichiometry has been most recently synthesized. Inspired by this experimental advance and exciting physics of nanoporous carbon nitrides, herein we conduct extensive density functional theory calculations to explore the electronic, optical and photocatalytic properties of the CN monolayer. In order to examine the dynamic stability and evaluate the mechanical and heat transport properties under ambient conditions, we employ state of the art methods on the basis of machine-learning interatomic potentials. The CN monolayer is found to be a direct band gap semiconductor, with a band-gap of 2.63 eV according to the HSE06 method. The obtained results confirm the dynamic stability, remarkable tensile strengths over 10 GPa and a low lattice thermal conductivity of ∼9.5 W m K for the CN monolayer at room temperature. The first absorption peak of the single-layer CN along the in-plane polarization is predicted to appear in the visible range of light. With a combination of high carrier mobility, appropriate band edge positions and strong absorption of visible light, the CN monolayer might be an appealing candidate for photocatalytic water splitting reactions. The presented results provide an extensive understanding concerning the critical physical properties of the CN nanosheets and also highlight the robustness of machine-learning interatomic potentials in the exploration of complex physical behaviors.