Machine learning assisted electrocaloric and pyroelectric performance of Ba_0.85Ca_0.15Ti_0.9Zr_0.1O₃ceramic for solid state refrigeration

Keyphrases

• Machine Learning 100%
• Electric Field (E-field) 100%
• Electrocaloric 100%
• Solid-state Refrigeration 100%
• Tetragonal Symmetry 100%
• Pyroelectric 100%
• Well-defined 33%
• Expected Outcomes 33%
• Machine Learning Approach 33%
• Environmentally Friendly 33%
• Cooling Technology 33%
• Scanning Electron Microscope 33%
• Machine Learning System 33%
• Cooling Power 33%
• Phase Transformation 33%
• Reliability Prediction 33%
• Cubic Symmetry 33%
• Efficient Prediction 33%
• X-ray Diffraction Pattern 33%
• Refrigerant Capacity 33%
• Polarization Value 33%
• Power Change 33%
• Entropy Change 33%
• Dielectric 33%
• Mixed Oxides 33%
• Isothermal Entropy 33%
• Orthorhombic Symmetry 33%
• Coercive Field 33%
• Remnant Polarization 33%
• Polarization Field 33%
• Ferroelectric Hysteresis 33%
• Maxwell Relation 33%
• Rietveld Refinement 33%
• Orthogonal Symmetries 33%
• Electrocaloric Strength 33%
• Adiabatic Temperature 33%

Material Science

• Scanning Electron Microscopy 100%
• Oxide Compound 100%
• Diffraction Pattern 100%
• X-Ray Diffraction 100%
• Ferroelectric Material 100%
• Dielectric Material 100%
• Rietveld Refinement 100%

Engineering

• Learning System 100%
• Electric Field 100%
• Dielectrics 33%
• Micrograph 33%
• Figure of Merit 33%
• Cooling Power 33%
• Isothermal 33%
• Learning Approach 33%
• Entropy Change 33%
• X-Ray Diffraction Pattern 33%
• Scanning Electron Microscope 33%
• Polarization Field 33%
• Adiabatic Temperature 33%
• Coercive Field 33%
• Cubic Symmetry 33%