Deep learning based quasi-continuum theory for structural prediction of water and Lennard-Jones fluid in confined environments

In this work, we propose a deep learning based quasi-continuum theory (DL-QT) to predict the concentration and potential profiles of a Lennard-Jones (LJ) fluid and water confined in a nano slit pore. In the first part, the deep learning model is built based on convolutional neural networks with the encoding-decoding process. The model is trained to relate the fluid properties to the fluid-fluid potential. We demonstrate that the well-trained model can accurately predict the fluid-fluid potential with a relative error < 5%. In the second part, the well-trained deep learning model is combined with the potential-based continuum theory to predict confined LJ fluid and water concentration profiles. We show that the DL-QT model has a robust predictive performance for LJ fluids confined in different channel sizes and under various thermodynamics states. In addition to predicting the properties of LJ fluid, we also demonstrate that the DL-QT model works well for confined water without introducing a coarse-grained potential model to the continuum theory.