Activity coefficients quantify the deviation of a real solution from an ideal one and are essential for chemical process design. To address the structural representation gaps and lack of thermodynamic consistency in existing models, we propose Gibbs–Helmholtz Graph External Attention (GHGEAT), a novel model that extracts global molecular features via an external attention mechanism and integrates the Gibbs–Helmholtz equation to establish rigorous physical constraints. GHGEAT achieves a Mean Absolute Error (MAE) of 0. 07 on a public internal data set of 21, 048 data points, outperforming established models. To further evaluate generalization capability, GHGEAT was tested on the IDAC₂026 data set, a new large-scale external data set constructed in this study by rigorously filtering the Brouwer data set to exclude training overlaps. On this challenging out-of-distribution data set, GHGEAT demonstrates superior performance with an MAE of 0. 3407 and an R2 of 0. 9343, representing an absolute R2 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.
Wang et al. (Mon,) studied this question.