Electronegativity Informed Graph Neural Networks for Superconducting Temperature Prediction with Generative Crystal Validation
. This work establishes a physically motivated and transferable workflow that connects chemical descriptor design, graph-based prediction, crystal generation, and first-principles validation for superconducting materials discovery.
Authors
- 熊清
- Zhaosheng Zhang (ORCID: https://orcid.org/0000-0002-4080-0884)
- Yanbo Liu (ORCID: https://orcid.org/0000-0002-4696-5209)
- Jiadong Liu
- Wutong Zhang
Institutions
- Hebei University (CN)
Publication Details
- Journal
- Inorganic Chemistry
- Published
- 2026-04-17
- DOI
- https://doi.org/10.1021/acs.inorgchem.6c01169
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 7.86
Funders
- National Natural Science Foundation of China
- Department of Education of Hebei Province
- Natural Science Foundation of Hebei Province
- Hebei University