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.

中文翻译：

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可解释的溶质-溶剂交互注意模块强化图学习架构，以提高无限稀释活度系数的预测精度


无限稀释活度系数（γ ^∞ ）是相平衡预测的重要热力学性质。在此，提出了一种溶质-溶剂交互注意模块来强化图学习架构，以构建 γ ^∞ 的准确预测模型。交互注意模块可以自适应地捕获溶质和溶剂之间的分子间交互信息。图学习架构获得的最终特征包括分子内和分子间特征以及温度相关参数的整体信息，这些信息被输入到 dropout 深度神经网络中进行预测。模型性能的多视图分析表明，与竞争模型相比，所提出的预测架构具有卓越的准确性和可靠性。此外，结果证明，通过所提出的注意力模块学习到的有价值的化学知识有助于提高模型的精度和可解释性。因此，所提出的 ln γ ^∞ 预测架构可以为绿色溶剂筛选和实际分离工艺开发提供可靠的工具。