Machine Learning-Assisted design of boron and nitrogen doped graphene nanosheets with tailored thermomechanical properties

Keyphrases

• Approximate Model 33%
• Assisted Design 100%
• Boron 33%
• Boron-doped Graphene 100%
• Boron-nitrogen 66%
• Co-doped Graphene 66%
• Coefficient of Determination 33%
• Doping Concentration 33%
• Electronic Devices 33%
• Energy Devices 33%
• Ensemble Model 33%
• External Force 33%
• External Heating 33%
• External Temperature 33%
• Failure Strain 33%
• Failure Stress 33%
• Gaussian Process Regression 33%
• Genetic Algorithm 33%
• Graphene 33%
• Graphene Nanosheets 66%
• High-performance Technology 33%
• Higher Coefficients 33%
• Innovative Performance 33%
• Inverse Problem 33%
• Kernel Approximation 33%
• Linear Regression Model 33%
• Machine Learning 100%
• Material Properties 33%
• Mechanical Properties 33%
• Model Regression 33%
• Modification Impact 33%
• Molecular Dynamics 33%
• Molecular Dynamics Simulation 66%
• Nanosheet Structure 33%
• Nanosheets 100%
• Nitrogen-doped Graphene Nanosheets 100%
• Overfitting 33%
• Prediction Accuracy 33%
• Property Values 33%
• Random Search Algorithm 33%
• Regression Neural Network 33%
• Regression Tree 33%
• Regression Tree Ensemble 33%
• Root Mean Square Error 33%
• Stress-strain 33%
• Structure Identification 33%
• Structure-property Relationships 33%
• Sufficient Evidence 33%
• Supervised Machine Learning 33%
• Support Vector Machine 33%
• Thermal Conductivity 33%
• Thermal Properties 33%
• Thermomechanical Properties 100%
• Tree Support 33%
• Tunable Thermo-mechanical Properties 33%
• Young's Modulus 33%

Material Science

• Boron 100%
• Graphene 42%
• Materials Property 14%
• Nanosheet 100%
• Nitrogen-Doped Graphene 100%
• Thermal Conductivity 14%
• Thermomechanical Property 100%
• Young's Modulus 14%

Chemical Engineering

• Graphene 100%
• Learning System 100%
• Nanosheet 100%
• Neural Network 14%
• Support Vector Machine 14%
• Thermal Conductivity 14%