Flat superhydrophilic coating and machine learning of co-sputtered coatings for wettability prediction

Maheswar Chaudhary, Ashok Thapa, Shalabh C. Maroo,
Flat superhydrophilic coating and machine learning of co-sputtered coatings for wettability prediction,
AI Thermal Fluids,
Volume 4,
2025,
100020,
ISSN 3050-5852,
https://doi.org/10.1016/j.aitf.2025.100020.
(https://www.sciencedirect.com/science/article/pii/S3050585225000199)
Abstract: Co-sputtering is a widely adopted technique for producing flat coatings with tailored wettability; however, it typically requires time-consuming and iterative tuning of deposition parameters through trial-and-error experiments. In this study, machine learning (ML) models are employed to predict the water contact angle (WCA) of co-sputtered coatings. A dataset comprising 86 samples was used to train and evaluate four ML models; of these, 73 data points were extracted from the literature, and 13 were experimentally obtained by depositing thin films on silicon wafers through co-sputtering. During the experimental data generation, a novel flat coating composed of silicon (Si) and magnesium (Mg) was developed, exhibiting superhydrophilic behavior with a WCA below 5°. The coating demonstrated short- and long-term durability, retaining its superhydrophilic characteristics for up to 30 days under ambient laboratory conditions. Remarkably, even after one year, the surface could recover its superhydrophilic state following a basic chemical cleaning. To enhance model performance, the hyperparameters of each ML model — linear regression, decision tree, random forest, and gradient boosting regression — were systematically optimized. Both hold-out validation and 10-fold cross validation approaches were employed to investigate the performance of each ML model. Among the models tested, random forest algorithm exhibited the highest prediction accuracy.
Keywords: Co-sputtering; Machine learning; Superhydrophilic; Contact angle