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

Xin Zhang; H. J. Yang; Wu Li; Jia Du

doi:10.1039/d6ta00098c

January 1, 2026Journal of Materials Chemistry A

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

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Key Points

The study aims to predict shear and bulk moduli of inorganic materials using interpretable machine learning techniques.
Utilized interpretable machine learning techniques with 34 key features.
Analyzed the relationships between features to predict elastic moduli.
Explored physical origins and interactions affecting elastic properties.
Achieved a prediction accuracy of R² = 0.892 for shear moduli and R² = 0.949 for bulk moduli.
Identified synergistic and compensatory interactions among features.
Facilitated rational design of elastic properties in inorganic materials.

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Implication

Interpretable ML uncovers elastic moduli in inorganic materials, suggesting new design strategies for properties.

Abstract

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.

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Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69c8c25dde0f0f753b39c9d0 https://doi.org/10.1039/d6ta00098c

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

Key Points

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Implication

Abstract

Discussion

Cite This Study

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

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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