GHGEAT: Gibbs–Helmholtz-Constrained Graph External Attention for Temperature-Dependent Activity Coefficient Prediction at Infinite Dilution
improvement of over 0.25 compared to second-best benchmark models. By combining hierarchical global feature extraction with thermodynamic consistency, GHGEAT ensures robust performance in continuous temperature interpolation and extrapolation, providing a physically meaningful decision-making tool for critical chemical engineering applications. The source code is available at https://github.com/Wang213-wq/GHGEAT.
Authors
- Jinlin Ye (ORCID: https://orcid.org/0009-0006-7353-7249)
- Wei Zhang
- Junhao Wang
Institutions
- Hebei University of Technology (CN)
- Artificial Intelligence in Medicine (Canada) (CA)
Publication Details
- Journal
- Journal of Chemical Information and Modeling
- Published
- 2026-03-31
- DOI
- https://doi.org/10.1021/acs.jcim.6c00199
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China