Neural Network Predicts Ion Concentration Profiles under Nanoconfinement
This provides a faster alternative for researchers in nanofluidics and electrochemistry, though it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of computationally expensive molecular dynamics simulations for predicting ion concentration profiles in nanochannels by proposing a neural network as a surrogate model, achieving high accuracy and demonstrating superior prediction over XGBoost.
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.