Kimika Teorikoa Seminar: Proton Transfer in CAGE 1:2 via Neural Network-based Force Field Simulations

In this work, we investigate the proton transfer mechanism in choline geranate (CAGE 1:2) ionic liquid using a combined approach of polarizable molecular dynamics (MD) and neural network force field (NNFF) simulations. Polarizable MD reveals the intricate hydrogen-bonding network but lacks explicit dynamic bond formation/breaking. In contrast, NNFF simulations provide deeper insight into proton transfer dynamics. We identify two distinct mechanisms: a direct transfer involving the geranate anion and another involving water. This study enhances the understanding of proton transfer in CAGE systems, highlighting the complementary strengths of polarizable MD and machine learning methods.