On-demand design of materials with enhanced dielectric properties <i>via</i> a machine learning-assisted materials genome approach
An on-demand design of silicon-containing arylacetylene resins with specific thermal and dielectric performance was achieved via the machine learning-assisted materials genome approach.
Authors
- Shihao Cao
- Chongyin Zhang (ORCID: https://orcid.org/0009-0009-5221-7010)
- Shuang Song (ORCID: https://orcid.org/0000-0002-4055-3975)
- Jiaping Lin (ORCID: https://orcid.org/0000-0001-9633-4483)
- Zhentao Zhang (ORCID: https://orcid.org/0000-0001-6708-1472)
- Haoxiang Lan
- Wanyu Tang
- Liang Gao (ORCID: https://orcid.org/0000-0002-5322-7343)
Institutions
- East China University of Science and Technology (CN)
- Shanghai Aerospace Automobile Electromechanical (China) (CN)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5ta01091h
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.59