Data-Driven Approach to Coarse-Graining Simple Liquids in Confinement

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 an 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 the sequential utilization of these two methods, the robustness of the iterative RE method is significantly augmented, leading to a remarkable enhancement in convergence

Innermost Ion Association Configuration Is a Key Structural Descriptor of Ionic Liquids at Electrified Interfaces

The structure of electric double layers (EDLs) is crucial for all types of electrochemical processes. While in dilute solutions EDL structure can be approximately treated within the Gouy–Chapman–Stern regime, in highly ionic electrolytes the description of EDL has been largely elusive. Here we study the EDL structure of an ionic liquid on a series of crystalline electrodes. Through molecular dynamics (MD) simulations, we observe strong intermolecular interaction among cations and anions and propose that the cation–anion association structure at the innermost layer is a key descriptor of the EDL. Using our recently developed electrochemical 3D atomic force microscopy (EC-3D-AFM) technique, we confirm the theoretical prediction and further find that the width of the first EDL is an experimental gauge of the ion association structure in that layer. We expect such ion association descriptors to be broadly applicable to a large range of highly ionic electrolytes on various electrode surfaces

Real-Space Charge Density Profiling of Electrode–Electrolyte Interfaces with Angstrom Depth Resolution

The accumulation and depletion of charges at electrode–electrolyte interfaces is crucial for all types of electrochemical processes. However, the spatial profile of such interfacial charges remains largely elusive. Here we develop charge profiling three-dimensional (3D) atomic force microscopy (CP-3D-AFM) to experimentally quantify the real-space charge distribution of the electrode surface and electric double layers (EDLs) with angstrom depth resolution. We first measure the 3D force maps at different electrode potentials using our recently developed electrochemical 3D-AFM. Through statistical analysis, peak deconvolution, and electrostatic calculations, we derive the depth profile of the local charge density. We perform such charge profiling for two types of emergent electrolytes, ionic liquids, and highly concentrated aqueous solutions, observe pronounced sub-nanometer charge variations, and find the integrated charge densities to agree with those derived from macroscopic electrochemical measurements