Fine-Tuning Graph Neural Networks via Active Learning: Unlocking the Potential of Graph Neural Networks Trained on Nonaqueous Systems for Aqueous CO2 Reduction

Abstract

Graph neural networks (GNNs) have revolutionized catalysis research with their efficiency and accuracy in modeling complex chemical interactions. However, adapting GNNs trained on nonaqueous data sets to aqueous systems poses notable challenges due to intricate water interactions. In this study, we proposed an active learning-based fine-tuning approach to extend the applicability of GNNs to aqueous environments. The geometry optimization and transition state search workflows are designed to reduce computational costs while maintaining DFT-level accuracy. Applied to the CO₂ reduction reaction, the workflow delivers a 2–3-fold acceleration in geometry optimization through a relaxed force threshold combined with DFT refinement. The versatility of the transition state search algorithm was demonstrated on key C–C coupling pathways, pinpointing *CO–*COH as the most energetically favorable pathway in aqueous systems of Cu and Cu-based Ag, Au, and Zn alloys. The Brønsted–Evans–Polanyi relationship remains robust under water-induced fluctuations, with alloyed metals such as Al, Ga, and Pd, along with Ag, Au, and Zn, exhibiting coupling efficiency comparable to that of Cu. Additionally, perturbation-based training on forces and energies extends the application of GNNs to aqueous ab initio molecular dynamics simulations, enabling efficient modeling of dynamical trajectories. This work presents novel approaches to adapting nonaqueous models for application in aqueous systems, highlighting GNNs’ potential in solvated environments and laying a foundation for accelerating predictions of catalytic mechanisms under realistic conditions.