Abstract
The rational design and high-throughput screening of sustainable CO(2) capture solvents are crucial for industrial decarbonization. Recent advances in artificial intelligence provide reliable and interpretable tools to accelerate this process. Here, we developed four neural network models trained on a curated dataset of 1,671 experimentally measured CO(2) solubility values spanning diverse solvent structures and operating conditions. Model development employed Bayesian hyperparameter optimization and 5-fold cross-validation to ensure consistent and robust performance. Among these models, the graph attention network (GAT) achieves the highest predictive accuracy (R (2) = 0.9837, RMSE = 0.0342) and exhibits strong robustness to perturbations in temperature, pressure, and solubility values. To enhance interpretability, we introduced a perturbation-based sensitivity analysis specifically tailored for graph-based models, enabling atomic-level attribution of solubility predictions. This analysis revealed polarizability as the dominant positive contributor, while heavy atom degree and hydrogen-bonding features act as primary negative contributors, consistent with physical absorption mechanisms. These findings demonstrate the potential of graph-based neural networks as accurate, robust, and interpretable tools for the data-driven design of green solvents for efficient CO(2) capture.