(Invited) Dynamics of Nitrogen Functionalization of Graphene

For graphene to be used efficiently in scientific and technological applications, its electronic properties need to be tailored to suit precise demands. The formation of a forbidden energy gap or the modification of the Fermi energy is achievable with functionalization techniques that simultaneously leave most of the atomic structure of graphene unchanged. Nitrogen functionalization is one of the most promising and studied types of treatment, in large part due to the proximity between carbon and nitrogen in the periodic table. It has already produced promising experimental results in the fields of high-sensibility biodetectors, field-effect transistors, solar cells, and supercapacitors, among others. To further understand this functionalization, we have performed an ab initio electronic structure study of different doping configurations and functionalization dynamics of nitrogen treatments of graphene. In particular, we have study the effect of simulation cell size used to carry our these calculations. We found that the inclusion of the special point K of the first Brillouin Zone in the momentum space sampling lowers formation energy results and should not be overlooked. Nudged Elastic Band calculations were also completed to study different incorporation mechanisms from an adsorbed state to in-plane doping. In the presence of native defects, low barriers of 0.55 eV and 0.46 eV were obtained. When no defects are present, much larger barriers between 3.70 eV to 4.38 eV were found, which suggests an external source of energy is required to complete the incorporation. In the second part of this presentation, we will report our result of using machine learning models to predict higher-level quantities. The goal of this work is to study the dependence of the Raman response of nitrogen functionalization of graphene. To simulate such a response, very large simulation cells need to be considered that would be out of reach for ab initio techniques, hence the use of machine learning methods to assist in the evaluation of the properties. Machine learning methods are now used more and more as a substitute for density functional theory calculations due to their low computational costs. However, in more advanced cases, relevant datasets are not always available, and the effort that would be necessary to generate the needed data suppress the advantages of using machine learning to speed up the calculations. Furthermore, the process of training a reliable model is not trivial and can also be expensive which should encourage the usage and diffusion of portable trained models. Figure 1

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