Decoding physical mechanisms governing elastic moduli in inorganic materials through interpretable machine learning

Interpretable ML with 34 key features predicts shear ( G ) and bulk ( K ) moduli ( R 2 = 0.892/0.949), reveals the physical origins, and uncovers synergistic and compensatory feature interactions for rational elastic property design of inorganic materials.