Correlation analysis of materials properties by machine learning: Illustrated with stacking fault energy from first-principles calculations in dilute fcc-based alloys

Keyphrases

• Material Analysis 100%
• First-principles Calculations 100%
• Material Properties 100%
• Machine Learning 100%
• Correlation Analysis 100%
• Stacking Fault Energy 100%
• Density Functional Theory 40%
• Energy Value 30%
• Mean Absolute Error 30%
• Machine Learning Prediction 30%
• Ni-based Alloy 20%
• NiPt 20%
• Al-Ni 20%
• Machine Learning Models 20%
• Machine Learning Algorithms 10%
• Alias 10%
• Shear Deformation 10%
• Statistical Significance 10%
• Experimental Verification 10%
• Model Performance 10%
• Attribute-based 10%
• Alloying Elements 10%
• Alloying Element Effect 10%
• Expected Mean 10%
• Cu-Ag 10%
• Rh-Pd 10%
• Theory Learning 10%
• Regression Algorithm 10%
• Periodic Table 10%
• Gaussian Process Regression 10%
• Ag-Au 10%
• Valence Electrons 10%
• Deformation Approach 10%

Material Science

• Materials Property 100%
• Crystal Defect 100%
• Density 40%
• Alloying 20%

Engineering

• High Stacking Fault Energy 11%
• Covalent Radius 11%