ArticleScientific reports2025
Machine learning approach to predict the viscosity of perfluoropolyether oils.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Predicting methane adsorption in coal and shale with white-box and black-box machine learning models.Scientific reports · 2026Article
- Comparative assessment of machine learning models for polymer solution viscosity prediction in enhanced oil recovery.Scientific reports · 2026Article
- Integrating experimental data and machine learning models for solubility prediction of yellow 23 in supercritical carbon dioxide.RSC advances · 2025Article
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2 authors.
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Abstract
Perfluoropolyethers (PFPEs) have attracted much attention due to their exceptional chemical stability, thermal resistance, and wide application in high-performance industries such as aerospace, semiconductors, and automotive engineering. One of the most important properties of PFPEs as lubricants is their viscosity. However, experimental determination of viscosity is time-consuming and expensive. In this study, four intelligent models, Multilayer Perceptron (MLP), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Adaptive Boost Support Vector Regression (AdaBoost-SVR), were used to predict the viscosity of perfluoropolyethers based on parameters such as temperature, density, and average polymer chain length. Statistical error analysis showed that the GPR model had higher accuracy than other models, achieving a root mean square error (RMSE) of 0.4535 and a coefficient of determination (R
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