  Modeling the ion concentration profile in nanochannel plays an important role
in understanding the electrical double layer and electroosmotic flow. Due to
the non-negligible surface interaction and the effect of discrete solvent
molecules, molecular dynamics (MD) simulation is often used as an essential
tool to study the behavior of ions under nanoconfinement. Despite the accuracy
of MD simulation in modeling nanoconfinement systems, it is computationally
expensive. In this work, we propose neural network to predict ion concentration
profiles in nanochannels with different configurations, including channel
widths, ion molarity, and ion types. By modeling the ion concentration profile
as a probability distribution, our neural network can serve as a much faster
surrogate model for MD simulation with high accuracy. We further demonstrate
the superior prediction accuracy of neural network over XGBoost. Lastly, we
demonstrated that neural network is flexible in predicting ion concentration
profiles with different bin sizes. Overall, our deep learning model is a fast,
flexible, and accurate surrogate model to predict ion concentration profiles in
nanoconfinement.
