Predicting Adsorbate-Solvent Interaction Energies in Zeolite Pores Using Convolutional Neural Networks with Attention Mechanisms

Trained on density functional theory (DFT)-derived interaction energies from a multiscale modeling workflow, the model achieves a mean absolute error of 0.08 eV for individual configurations and 0.04 eV for ensemble-averaged solvation energies, approaching DFT accuracy while substantially reducing computational cost. Cross-validation (CV) using simulation-system, solvent-composition, and pore-type splits demonstrates predictive performance on held-out systems within the represented chemical domain and across the solvent and pore conditions considered in this study.

Analysis of spatial attention maps and feature attributions reveals that the model emphasizes adsorbate-proximal solvent structures and hydrogen-bonding interactions, consistent with physical intuition. This framework enables efficient and interpretable prediction of solvation interaction energies in confined systems and provides a scalable pathway for mapping structural information sampled from force-field-MD to DFT targets.