Predicting the Bandgap of Graphene Based on Machine Learning

Over the past decade, two-dimensional materials have become a research hotspot in chemistry, physics, materials science, and electrical and optical engineering due to their excellent properties. Graphene is one of the earliest discovered 2D materials. The absence of a bandgap in pure graphene limits its application in digital electronics where switching behavior is essential. In the present study, researchers have proposed a variety of methods for tuning the graphene bandgap. Machine learning methodologies have demonstrated the capability to enhance the efficiency of materials research by automating the recording of characteristic parameters from the discovery and preparation of 2D materials, property calculations, and simulations, as well as by facilitating the extraction and summarization of governing principles. In this work, we use first principle calculations to build a dataset of graphene band gaps under various conditions, including the application of a perpendicular external electric field, nitrogen doping, and hydrogen atom adsorption. Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP) Regression were utilized to successfully predict the graphene bandgap, and the accuracy of the models was verified using first principles. Finally, the advantages and limitations of the three models were compared.