Understanding Epitaxy of Graphene: from Experimental Observation to Density Functional Theory and Machine Learning

Due to enormous applications of large-area graphene with high quality, the epitaxial growth strategies have drawn a plethora of attention. However, the bottleneck in the production of graphene has caused delayed development in recent years, which is owing to the poor understanding of interaction mechanisms between graphene and the underlying metallic and non-metallic substrate. To understand the thermodynamics of graphene-substrate interface and growth kinetics, accurate density functional theory (DFT) calculations have been proved as an effective way, in terms of cost and time, compared with traditional experimental methods, which can calculate the interaction between graphene and substrates, helping us to better understand the practical phenomena. Here, we show the use of DFT methods to evaluate both van der Waals interaction and covalent bonding. Many of computational results fit well with the experimental observations. To address the relative low accuracy and small computation capacity (number of atoms) of common DFT models, we suggest that the machine learning (ML) methods will be a fresh impetus for epitaxial growth strategy of graphene, which put forward effective interpretations for complicated interconnections and correlations among the properties, thereby enabling ML a promising strategy for understanding, design, and synthesis of graphene over other 2D materials.