Superhydrophobic Polymer Topography Design Assisted by Machine Learning Algorithms

Abstract

Superhydrophobic surfaces have been largely achieved through various surface topographies. Both empirical and numerical simulations have been reported to help understand and design superhydrophobic surfaces. Many such successful surfaces have also been achieved using bioinspired and biomimetic designs. Despite this, identifying the right surface texture to meet the requirements of specific applications is not a straightforward task. Here, we report a hybrid approach that includes experimental methods, numerical simulations, and machine learning (ML) algorithms to create design maps for superhydrophobic polymer topographies. Two design objectives to investigate superhydrophobic properties were the maximum water contact angle (WCA) and Laplace pressure. The design parameters were the geometries of an isotropic pillar structure in micrometer and sub-micrometer length scales. The finite element method (FEM) was validated by the experimental data and employed to generate a labeled dataset for ML training. Artificial neural network (ANN) models were then trained on the labeled database for the topographic parameters (width W, height H, and pitch P) with the corresponding WCA and Laplace pressure. The ANN models yielded a series of nonlinear relationships between the topographic design parameters and the WCA and Laplace pressure and substantial differences between the micrometer and sub-micrometer length scales. Design maps that span the topography design parameters provide optimal design or tradeoff parameters. This research demonstrates the potential of ANN as a rapid design tool for surface topography exploration.