Interpretable ML reveals physics phase changes in material corrosion resistance
bravo_abad · x · 2026-08-20
Kei Masuhara and coauthors use interpretable ML to study corrosion resistance in steam-coated aluminum. Addressing the challenge of correlated variables (thickness, morphology, etc.), they train a random forest model on physically motivated descriptors (R²=0.67). SHAP analysis identifies surface morphology and film thickness as dominant variables. Accumulated Local Effects (ALE) reveal something deeper: the role of morphology changes as the coating develops. In early-growth films, morphology reflects protective surfaces, but this shifts in later stages, successfully detecting a change in the governing physics.
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