review · Heliyon
Graphene remains a major focus in two-dimensional materials research, with significant attention directed towards exploiting its optical and electronic properties for optoelectronic devices. Over the last two decades, computational studies have relied heavily on density functional theory alongside emerging machine learning frameworks to predict these behaviours. An evaluation of existing literature provides data on the bond lengths, band gaps, and thermodynamic stability of doped graphene systems, showing that findings vary considerably according to dopant selection, basis sets, exchange-correlation functionals, and input accuracy. Assessing machine learning potentials highlights both their predictive utility and their uncertainties. Further research is still needed to model thermal properties, graphene heterostructures, and superconducting states, as well as to optimise computational models for dependable device development.
Accurate computer simulations allow materials scientists to predict how graphene will behave without relying purely on costly, trial-and-error laboratory experiments. By highlighting inconsistencies across computational models and the strengths of machine learning, this evaluation helps both academic and industrial researchers establish more reliable foundations for designing nanoscale optical and electronic devices.
This work informs early-stage computational design for optoelectronic devices, serving device engineers and computational materials scientists in industry and academia. Because it evaluates theoretical models and literature data rather than fabricated prototypes, the technology sits at an early, pre-experimental stage of the development pipeline.
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Graphene has received tremendous attention among diverse 2D materials because of its remarkable properties. Its emergence over the last two decades gave a new and distinct dynamic to the study of materials, with several research projects focusing on exploiting its intrinsic properties for optoelectronic devices. This review provides a comprehensive overview of several published articles based on density functional theory and recently introduced machine learning approaches applied to study the electronic and optical properties of graphene. A comprehensive catalogue of the bond lengths, band gaps, and formation energies of various doped graphene systems that determine thermodynamic stability was reported in the literature. In these studies, the peculiarity of the obtained results reported is consequent on the nature and type of the dopants, the choice of the XC functionals, the basis set, and the wrong input parameters. The different density functional theory models, as well as the strengths and uncertainties of the ML potentials employed in the machine learning approach to enhance the prediction models for graphene, were elucidated. Lastly, the thermal properties, modelling of graphene heterostructures, the superconducting behaviour of graphene, and optimization of the DFT models are grey areas that future studies should explore in enhancing its unique potential. Therefore, the identified future trends and knowledge gaps have a prospect in both academia and industry to design future and reliable optoelectronic devices.
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DOI: 10.1016/j.heliyon.2023.e14279
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