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Upcoming Seminar

Ion transport under nanoconfinement: Insights from machine learning-based molecular dynamics
Dr. Kara Fong, Caltech

February 4, 2026
4:00pm-5:00pm in HSCI-105

Abstract

Ion transport through nanoscale pores and channels is an essential process in a range of both natural and engineered systems, from supercapacitor electrodes and desalination membranes to transmembrane proteins. Our fundamental understanding of nanoconfined electrolytes, however, lags far behind that of bulk electrolytes. This challenge stems in part from the limitations of standard simulation methodologies, namely that classical molecular dynamics force fields are not parameterized to accurately capture the complex interplay of ion-ion, ion-water, and ion-pore interactions governing the behavior of confined electrolytes.

Herein, we explore the transport properties of a prototypical nanoconfined electrolyte, aqueous NaCl in graphene slit pores, using machine learning-based molecular dynamics simulations. The machine learning potential driving these simulations allows us to reach density functional theory-level accuracy at orders of magnitude lower cost than conventional ab initio molecular dynamics.

We find that ionic conductivity decreases as an electrolyte becomes increasingly confined. By decomposing the total solution conductivity into the constituent Onsager transport coefficients, we show how this trend arises from changes in both ion self-diffusion (Nernst-Einstein transport) as well as non-ideal, correlated ion motion.

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