Understanding the Dielectric Relaxation of Liquid Water Using Neural Network Potential and Classical Pairwise Potential

Understanding the role of hydrogen bond networks in determining the relaxation dynamics is essential for understanding natural phenomena in liquid water. Classical pairwise additive models have been widely utilized for elaborating the underlying mechanism behind the relaxation phenomena. However, they have shown their limits due to either the absence or inaccurate descriptions of many-body and medium-to-long-range interactions. This work demonstrates that the Deep Potential Molecular Dynamics (DPMD) model help calculate the dielectric relaxation at the accuracy of the first-principles simulations. The DPMD model outperforms the classical force field (SPC/Fw) in predicting dielectric spectra. Analyzing the simulation results, we discover that the improvement does not stem from accurately representing the tetrahedral order. Instead, including inherent many-body interactions and intramolecular dynamics makes water molecules adapt to their local environments, lowering the potential energy barrier of reorientation.