Neural network modelling of the wettability of a surface grooved with the nanoscale pillars

Abstract

An array of the nanoscale grooves is often engraved to tune the wettability of a surface. Predicting the wettability of such a grooved surface is challenging because of the complex interplay of the geometry and energetics of the surface and temperature. Herein, we present an artificial neural network (ANN) model which predicts the wettability of a surface periodically patterned with the rectangular pillars. The present ANN model performs well against an extensive set of Monte Carlo simulations using the lattice gas model.