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.
Authors
- Wu Li (ORCID: https://orcid.org/0000-0003-3854-4268)
- Xin Zhang (ORCID: https://orcid.org/0000-0001-8663-5637)
- H. J. Yang (ORCID: https://orcid.org/0009-0004-8274-9863)
- Jia Du (ORCID: https://orcid.org/0000-0003-3638-5646)
Institutions
- Beijing University of Technology (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1039/d6ta00098c
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00