An Interpretable Solute–Solvent Interactive Attention Module Intensified Graph-Learning Architecture toward Enhancing the Prediction Accuracy of an Infinite Dilution Activity Coefficient

The infinite dilution activity coefficient (γ∞) is a significant thermodynamic property for phase
equilibrium prediction.
Herein, a solute–solvent interactive attention module is proposed
to intensify the graph-learning architecture for construction of an
accurate predictive model for γ∞. The interactive
attention module can adaptively capture the intermolecular interactive
information between solute and solvent. The final features obtained
by the graph-learning architecture include overall information on
the intra- and inter-molecular features and temperature-dependent
parameters, which are fed into the dropout deep neural network to
make predictions. Multiview analysis of the model performance demonstrates
that the proposed predictive architecture exhibits superior accuracy
and reliability compared to the competitive model. Furthermore, the
results prove that the valuable chemical knowledge learned through
the proposed attention module contributes to improving the precision
and interpretability of the model. As such, the proposed ln γ∞ predictive architecture could provide a reliable tool
for green solvent screening and actual separation process development.