Machine Learning-Enabled Repurposing and Design of Antifouling Polymer Brushes

Rational development of antifouling materials is of great importance for fundamental research and real-world applications. However, current experimental designs and computational modelings of antifouling materials still retain empirical flavor due to the data complexity of polymers and their associated structures/properties. In this work, we developed a data-driven, machine learning workflow, in combination with an in-house benchmark dataset of antifouling polymer brushes, to discover the potential antifouling property of existing polymer brushes using the descriptor-based artificial neural network (ANN) model and design the new antifouling polymer brushes using the group-based supporting vector regression (SVR) model. The resultant two machine learning models not only demonstrated their reliability, predictivity, and applicability, but also established the composition-structure-property relationships using both descriptors and functional groups. Finally, we synthesized different repurposed and newly designed polymer brushes, as predicted by ANN and SVR models, all of which exhibited excellent surface resistance to protein adsorption from undiluted human blood serum and plasma at optimal film thicknesses. Overall, our data-driven machine learning models can be used as an intelligent tool for determining, repurposing, and designing new superior antifouling materials beyond polymer brushes.