CAS Key Laboratory of Bio-inspired Materials and Interfacial Science, Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Beijing, 100190, P. R. China.
University of Chinese Academy of Sciences, Beijing, 100049, P. R. China.
Small. 2022 Oct;18(41):e2203264. doi: 10.1002/smll.202203264. Epub 2022 Sep 7.
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.
具有“荷叶效应”的超疏水表面在日常生活和工业中有广泛的应用，例如自清洁、防冻和防腐蚀。然而，由于复杂的表面形貌，传统的理论模型很难可靠地预测设计的超疏水表面是否具有“荷叶效应”。在这里，通过设计一组关于纳米级粗糙度和微观形貌的描述符，并结合表面疏水性修饰，展示了一种可靠的机器学习 (ML) 模型，可以准确预测固体表面的“荷叶效应”。几何和数学描述符与灰度共生矩阵 (GLCM) 相结合，为准确描述复杂形貌提供了一种可行的解决方案。此外，通过特征重要性和 Shapley-additive-explanations (SHAP) 分析来揭示水滴在超疏水表面上的附着趋势，从而打开“黑箱”。对制造的超疏水表面的准确预测强烈证实了 ML 模型的可扩展性。这种方法可以很容易地推广到筛选具有其他特性的固体表面。
