Predicting the Bandgap of Graphene Using Machine Learning | #Sciencefather #Researcherawards

 


Introduction

Two-dimensional materials have revolutionized modern research in chemistry, physics, materials science, and electronic engineering due to their exceptional physical and chemical properties. Among them, graphene has attracted particular attention for its remarkable electrical conductivity, thermal stability, and mechanical strength. However, its zero bandgap nature restricts its application in digital electronics, where on–off switching characteristics are vital. Researchers have thus focused on various strategies to engineer and control the graphene bandgap for advanced electronic and optoelectronic devices.

Graphene Bandgap Engineering

The absence of a natural bandgap in pristine graphene poses challenges for its use in transistors and semiconductors. To overcome this, numerous bandgap engineering techniques have been developed, such as doping, electric field application, and surface functionalization. Nitrogen doping introduces localized states that modify the electronic structure, while hydrogen adsorption alters hybridization, enabling tunable electronic properties. Applying a perpendicular external electric field further allows dynamic modulation of the bandgap, paving the way for graphene-based field-effect transistors.

Role of Machine Learning in Materials Research

Machine learning (ML) has emerged as a transformative tool in materials science, offering predictive capabilities that accelerate material discovery and design. By automating data analysis and feature extraction, ML models efficiently correlate structural parameters with material properties. In graphene research, ML helps optimize bandgap prediction by learning complex relationships from simulation and experimental datasets, significantly reducing the time and cost associated with traditional computational methods.

Dataset Construction Using First Principles

A robust dataset is critical for accurate ML-based predictions. In this study, first-principles calculations based on density functional theory (DFT) were employed to construct a dataset of graphene bandgaps under different physical and chemical conditions. Parameters such as electric field strength, nitrogen concentration, and hydrogen coverage were systematically varied. The resulting dataset provided the foundation for training and validating machine learning models, ensuring a reliable and physically grounded prediction process.

Machine Learning Models for Bandgap Prediction

Three prominent ML regression models—Support Vector Machine (SVM), Random Forest (RF), and Multi-Layer Perceptron (MLP)—were employed to predict graphene’s bandgap. Each model demonstrated distinct advantages: SVM excelled in handling non-linear relationships, RF provided strong interpretability and resistance to overfitting, while MLP captured complex data patterns through deep neural architectures. The models’ predictions were cross-validated against first-principles calculations to ensure high accuracy and robustness.

Comparative Analysis and Future Perspectives

Comparing the three ML models revealed complementary strengths. While SVM offered precise predictions for small datasets, RF achieved excellent generalization across diverse conditions, and MLP provided scalability for larger datasets. These findings highlight the potential of integrating ML with quantum simulations for accelerated material innovation. Future research may expand to include other 2D materials and hybrid ML–quantum models, enabling automated discovery pipelines for next-generation electronic materials.

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#Sciencefather, #Reseacherawards, #GrapheneResearch, #2DMaterials, #BandgapEngineering, #MachineLearning, #MaterialScience, #GrapheneBandgap, #ArtificialIntelligence, #PhysicsResearch, #ComputationalChemistry, #QuantumSimulations, #FirstPrinciples, #Nanotechnology, #DataDrivenScience, #MLinMaterials, #GrapheneApplications, #ElectricFieldEffects, #DopingStudies, #NeuralNetworks, #SupportVectorMachine, #RandomForest,

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