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.

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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

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article

GHGEAT: Gibbs–Helmholtz-Constrained Graph External Attention for Temperature-Dependent Activity Coefficient Prediction at Infinite Dilution

Jinlin Ye, Wei Zhang, Junhao Wang
Journal of Chemical Information and Modeling
Machine Learning in Materials Science
article

GHGEAT: Gibbs–Helmholtz-Constrained Graph External Attention for Temperature-Dependent Activity Coefficient Prediction at Infinite Dilution

Jinlin Ye, Wei Zhang, Junhao Wang
article en

Abstract

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.

Journal of Chemical Information and ModelingVol. 66(7)
Hebei University of Technology (CN), Artificial Intelligence in Medicine (Canada) (CA)
National Natural Science Foundation of China
Peace, Justice and strong institutions
Openalex Percentile: Top 32%
Machine Learning in Materials Science
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