(169ar) Capturing Realistic Double Layer Cation Properties in Classical MD Using Aimd-Guided Potential Fitting

Understanding electrochemical processes is fundamental for advancing various technological applications, from energy storage to catalysis. The distribution and behavior of cations within the electric double layer (EDL) play a crucial role in these processes. Ab initio molecular dynamics (AIMD) simulations offer a powerful tool for studying these phenomena with accurate electronic structure analysis. However, despite their strengths, AIMD simulations also come with inherent limitations, particularly when investigating cation distributions throughout the entire double layer. The high computational cost of these quantum mechanical (QM) methods, combined with the slow dynamics of ions and water when diffusing to and from the electrode surface, severely hinders the usefulness of AIMD when exploring ion distributions in the EDL. Classical models, on the other hand, provide an alternative approach that can efficiently capture the dynamic behavior of cations over longer timescales and larger system sizes. We have developed a dynamic charge approach (“QDyn”), which takes advantage of classical electrostatic principles, to rapidly allow charge in the (non-neutral) metal to move in response to electrolyte dynamics. This method allows simulation of 10’s of nanoseconds of dynamics for systems containing 10,000’s of metal and electrolyte atoms over a few days on a modest lab computational resource. However, to ensure the reliability and accuracy of the model in capturing the behavior of ions within the EDL, it is essential to fit the metal ion potentials. Fitting these potentials to gas phase or micro solvated QM calculations poses challenges due to their limited accuracy in sampling many water configurations close to the system that would be present in the classical model. To refine the metal ion potentials, we used meta-dynamics biased AIMD to confine the ions close to the metal surface. This restraint was used to keep the ion close enough to the metal to measure the repulsive forces while improving sampling of the water solvation in this region. Fitting the behavior and repulsion of the ion meta-dynamics biased AIMD to a classical simulation with the same bias enabled a more accurate classical model which can then be used to better study the behavior of ions close to the interface. The repulsive trends of the ions are compared down the periodic table and across metals hinting at a systematic approach for estimating more accurate potentials without relying on individual AIMD simulation for every ion. Additionally, EDL properties of ions are studied by predicting the ion distribution and dielectric constant of the double layer as a function of ion concentration, identity, and metal potential. This comprehensive approach provides insights into the behavior of ions near the interface. Finally, recommendations on best practices for utilizing AIMD to fit classical potentials are presented, offering guidance for future research utilizing classical molecular dynamics.