A Machine Learning-Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite-Based Materials

Keyphrases

• 10-fold Cross Validation 33%
• Acceptable Accuracy 33%
• AFLOW 33%
• Brittle Behavior 33%
• Data Characteristics 33%
• Density Functional Calculations 33%
• Ductile Behavior 33%
• Feature Generation 33%
• Feature Processing 33%
• Feature Testing 33%
• Feature Training 33%
• High Mechanical Stability 33%
• Large-scale Screening 33%
• Machine Learning 100%
• Machine Learning Based 33%
• Machine Learning Models 100%
• Machine Learning Techniques 33%
• Machine Learning Workflow 33%
• Mechanical Performance 33%
• Mechanically Stable 100%
• Perovskite Materials 100%
• Pugh's Criteria 33%
• R2 Score 33%
• Screening Criteria 33%
• Screening Process 66%
• Stable Perovskite 100%
• Thermodynamically Stable 33%
• Validation Process 33%

Material Science

• Crystal Structure 100%
• Density 50%
• Mechanical Stability 50%