ArticleThe journal of physical chemistry. B2025
A Machine Learning Model for the Prediction of Water Contact Angles on Solid Polymers.
Article in The journal of physical chemistry. B, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A robust machine learning framework for predicting contact angle in nano-assisted chemical EOR.Scientific reports · 2026Article
- Article
- Arabinoxylan-Based Bioplastic from Wheat Bran: A Promising Replacement for Synthetic Plastics.Polymers · 2025Article
- Interfacial Dynamics in the Fabrication of Various Concave Hydrogel Discs for Enhanced Biosensing.Polymers · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The interaction between water and solid surfaces is an active area of research, and the interaction can be generally defined as hydrophobic or hydrophilic depending on the level of wetting of the surface. This wetting level can be modified, among other methods, by applying coatings, which often modify the chemistry of the surface. With the increase in available computing power and computational algorithms, methods to develop new materials and coatings have shifted from being heavily experimental to including more theoretical approaches. In this work, we use a range of experimental and computational features to develop a supervised machine learning (ML) model using the XGBoost algorithm that can predict the water contact angle (WCA) on the surface of a range of solid polymers. The mean absolute error (MAE) of the predictions is below 5.0°. Models composed of only computational features were also explored with good results (MAE < 5.0°), suggesting that this approach could be used for the "bottom-up" computational design of new polymers and coatings with specific water contact angles.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.