Optimizing a machine learning design of dielectric properties in lead-free piezoelectric ceramics

Keyphrases

• Multilayer Perceptron 100%
• Electrical Properties 100%
• Dielectric Properties 100%
• Lead-free piezoelectric Ceramics 100%
• Machine Learning Design 100%
• Machine Learning Techniques 66%
• Neural Network Regression 66%
• Non-dominated Sorting Genetic Algorithm (NSGA-II) 66%
• Piezoelectric Ceramics 66%
• Knee 33%
• Machine Learning 33%
• Correlation Coefficient 33%
• Machine Learning Models 33%
• Two-machine 33%
• Root Mean Square Error 33%
• Random Forest Regression 33%
• Lead-free 33%
• Dielectric Loss 33%
• Calcium Titanate 33%
• Dielectric Measurements 33%
• TiO3 33%
• Training Machine 33%
• Material Development 33%
• Permittivity 33%
• Machining Optimization 33%
• Ca2+ Concentration 33%
• Neural Network Regression Model 33%
• Comprehensive Dataset 33%
• Optimization Procedure 33%
• Novel Methodology 33%
• Pareto Curve 33%

Engineering

• Dielectrics 100%
• Piezoelectric 100%
• Perceptron 75%
• Non-Dominated Sorting Genetic Algorithm II 50%
• Random Forest 25%
• Pareto Curve 25%
• Root-Mean-Squared Error 25%
• Dielectric Loss 25%
• Optimal Value 25%
• Optimisation Procedure 25%
• Material Development 25%
• Targeted Material 25%
• Machine Learning Technique 25%

Material Science

• Dielectric Property 100%
• Lead-Free Piezoelectric Ceramics 100%
• Multilayer 100%
• Dielectric Material 66%
• Piezoelectric Ceramics 66%
• Permittivity 33%