arXiv subjects

Haiyi Wu

Publications and source records attributed to Haiyi Wu.

5 recordsLinked to original sources

arXiv2026

Liquid structure adjacent to solid surfaces follows the superposition principle

Liquid structure at solid-liquid interfaces is critical for many natural and engineered processes ranging from biological signal transduction to electrochemical energy conversion. Advanced experimental and computational methods have provided insights into the structure of liquids adjacent to planar substrates at the nanoscale. However, realistic solid-liquid interfaces are inevitably inhomogeneous across multiple length scales, presenting a complexity that surpasses the capabilities of existing approaches. Here we bridge the complexity gap by discovering and utilizing a hitherto hidden principle of interfacial liquid--superposition. Experimentally, we use 3D atomic force microscopy (3D-AFM) to image the interfacial structure of a wide range of organic and aqueous solvents and electrolytes, uncovering universal liquid density oscillations and emergent liquid layer reconfigurations at heterogeneous substrate sites. We further develop an analytical model, coined solid-liquid superposition (SLS), which solves the interfacial liquid density distribution based on a key descriptor: the effective total correlation function (ETCF) between a liquid molecule and nearby solid atoms. SLS not only explains all the experimentally observed interfacial liquid distribution profiles from the angstrom to near-micron scale, but also predicts more precise atomic-scale interference patterns which are further corroborated by molecular dynamics (MD) simulations. This study unveils a key structural descriptor of interfacial liquids, and establishes a theoretical framework for rapidly and accurately predicting liquid structures adjacent to solid surfaces with arbitrary morphology and size scale.

physics.chem-ph↗

arXiv2023

A 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 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.

cond-mat.stat-mech↗

arXiv2020

Drying of porous media by concurrent drainage and evaporation: A pore network modeling study

Drainage and evaporation can occur simultaneously during the drying of porous media, but the interactions between these processes and their effects on drying are rarely studied. In this work, we develop a pore network model that considers drainage, evaporation, and rarefied multi-component gas transport in porous media with nanoscale pores. Using this model, we investigate the drying of a liquid solvent-saturated porous medium enabled by the flow of purge gas through it. Simulations show that drying progresses in three stages, and the solvent removal by drainage effects (evaporation effects) becomes increasingly weak (strong) as drying progresses through these stages. Interestingly, drainage can contribute considerably to solvent removal even after evaporation effects become very strong, especially when the applied pressure difference across the porous medium is low. We show that these phenomena are the results of the coupling between the drainage and evaporation effects and this coupling depends on the operating conditions and the stage of drying.

cond-mat.soft↗

arXiv2019

Predicting Effective Diffusivity of Porous Media from Images by Deep Learning

We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topology, large variation of porosity (0.28-0.98), and effective diffusivity spanning more than one order of magnitude (), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, especially for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed analysis of the performance of CNN models in the present work.

physics.geo-ph↗

arXiv2016

Super-Diffusive Gas Recovery from Nanopores

Understanding the recovery of gas from reservoirs featuring pervasive nanopores is essential for effective shale gas extraction. Classical theories cannot accurately predict such gas recovery and many experimental observations are not well understood. Here we report molecular simulations of the recovery of gas from single nanopores, explicitly taking into account molecular gas-wall interactions. We show that, in very narrow pores, the strong gas-wall interactions are essential in determining the gas recovery behavior both quantitatively and qualitatively. These interactions cause the total diffusion coefficients of the gas molecules in nanopores to be smaller than those predicted by kinetic theories, hence slowing down the rate of gas recovery. These interactions also lead to significant adsorption of gas molecules on the pore walls. Because of the desorption of these gas molecules during gas recovery, the gas recovery from the nanopore does not exhibit the usual diffusive scaling law (i.e., the accumulative recovery scales as $R \sim t^{1/2}$ but follows a super-diffusive scaling law $R \sim t^n$ ($n>0.5$), which is similar to that observed in some field experiments. For the system studied here, the super-diffusive gas recovery scaling law can be captured well by continuum models in which the gas adsorption and desorption from pore walls are taken into account using the Langmuir model.

physics.flu-dyn↗