Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

It is widely recognized that surface roughness plays an important role in ice adhesion strength, although the correlation between the two is far from understood. In this paper, two approaches, molecular dynamics (MD) simulations and machine learning (ML), were utilized to study the nanoscale intrinsic ice adhesion strength on rough surfaces. A systematic algorithm for making random rough surfaces was developed and the surfaces were tested for their ice adhesion strength, with varying interatomic potentials. Using MD simulations, the intrinsic ice adhesion strength was found to be significantly lower on rougher surfaces, which was attributed to the lubricating effect of a thin quasi-liquid layer. An increase in the substrate-ice interatomic potential increased the thickness of the quasi-liquid layer on rough surfaces. Two different ML algorithms, regression and classification, were trained using the results from the MD simulations, with support vector machines (SVM) emerging as the best for classifying. The ML approach showed an encouraging prediction accuracy, and for the first time shed light on using ML for anti-icing surface design. The findings provide a better understanding of the role of nanoscale roughness in intrinsic ice adhesion and suggest that ML can be a powerful tool in finding materials with a low ice adhesion strength.

Automated summary

In this paper, the role of surface roughness in ice adhesion strength was investigated using molecular dynamics simulations and machine learning. It was found that a thin quasi-liquid layer on rough surfaces reduced the ice adhesion strength, and support vector machines emerged as the best classification tool in machine learning for this application. The study highlights the potential of using machine learning in anti-icing surface design and provides a better understanding of the intrinsic ice adhesion on rough surfaces.