Ion (de)solvation is a common process that occurs across a wide variety of interfacial phenomena including corrosion and electrochemical energy storage. Understanding the factors that control ion (de)solvation kinetics is important for engineering key performance metrics such as alloy corrosion rates and the charge/discharge rates of electrochemical energy storage devices. In this talk, we introduce a set of recently developed dynamic metrics for ion (de)solvation kinetics which offers a new framework for understanding and discussing ion transfer processes. Our metrics are predicated on the dynamic bonding that takes place between dissolved ions and solvent molecules, effectively describing the strength and chemical nature of ion-solvent bonds as well as the effective kinetic barrier for solvent molecule attachment. To demonstrate the descriptive power of our new metrics, we applied them to a diverse set of metallic ions that were simulated in water using first principles molecular dynamics. Our analysis shows that the new set of dynamic metrics not only correctly encodes key physical behavior, but they can also explain trends in ion behavior that are challenging to address using conventional static descriptors. Beyond aiding the description and discussion of ion transport kinetics, our metrics provide useful targets for the development of machine learning-based force fields to ensure kinetic accuracy for ion transport modeling.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was supported with Laboratory Directed Research and Development funding under Project 20-SI-004.