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Prediction of the Lotus Effect on Solid Surfaces by Machine Learning

2022; Wiley; Volume: 18; Issue: 41 Linguagem: Inglês

10.1002/smll.202203264

1613-6829

Xiao Dong He, Kaihua Zhang, Xianghui Xiong, Yuepeng Li, Xizi Wan, Zijia Chen, Yixuan Wang, Xue-Tao Xu, Mingqian Liu, Ying Jiang, Shutao Wang,

Surface Roughness and Optical Measurements

Abstract Superhydrophobic surfaces with the “lotus effect” have wide applications in daily life and industry, such as self‐cleaning, anti‐freezing, and anti‐corrosion. However, it is difficult to reliably predict whether a designed superhydrophobic surface has the “lotus effect” by traditional theoretical models due to complex surface topographies. Here, a reliable machine learning (ML) model to accurately predict the “lotus effect” of solid surfaces by designing a set of descriptors about nano‐scale roughness and micro‐scale topographies in addition to the surface hydrophobic modification is demonstrated. Geometrical and mathematical descriptors combined with gray level cooccurrence matrices (GLCM) offer a feasible solution to the puzzle of accurate descriptions of complex topographies. Furthermore, the “black box” is opened by feature importance and Shapley‐additive‐explanations (SHAP) analysis to extract waterdrop adhesion trends on superhydrophobic surfaces. The accurate prediction on as‐fabricated superhydrophobic surfaces strongly affirms the extensionality of the ML model. This approach can be easily generalized to screen solid surfaces with other properties.