Prediction of the elastic properties and electrical resistance of halide glass based on interpretable machine learning
This paper employs regression models based on machine learning to predict the elastic properties and electrical resistance of halide glass, unveiling the composition–property map and enabling swift optimization and rapid iteration.
Authors
- Xiangfu Wang (ORCID: https://orcid.org/0000-0003-2365-3357)
- Jinhua Zhou (ORCID: https://orcid.org/0000-0001-5668-3048)
Institutions
- Nanjing University of Posts and Telecommunications (CN)
Publication Details
- Journal
- Journal of Materials Chemistry C
- Published
- 2025-11-08
- DOI
- https://doi.org/10.1039/d5tc03267a
- Primary Topic
- Glass properties and applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00