  We propose a data-driven framework for identifying coarse-grained (CG)
Lennard-Jones (LJ) potential parameters in confined systems for simple liquids.
Our approach involves the use of a Deep Neural Network (DNN) that is trained to
approximate the solution of the Inverse Liquid State (ILST) problem for
confined systems. The DNN model inherently incorporates essential physical
characteristics specific to confined fluids, enabling accurate prediction of
inhomogeneity effects. By utilizing transfer learning, we predict single-site
LJ potentials of simple multiatomic liquids confined in a slit-like channel,
which effectively replicate both the fluid structure and molecular force of the
target All-Atom (AA) system when the electrostatic interactions are not
dominant. In addition, we showcase the synergy between the data-driven approach
and the well-known Bottom-Up coarse-graining method utilizing Relative-Entropy
(RE) Minimization. Through sequential utilization of these two methods, the
robustness of the iterative RE method is significantly augmented, leading to a
remarkable enhancement in convergence.
