(untitled) (International journal of artificial intelligence, 2025)
The integration of machine learning (ML) techniques in material science has revolutionized the analysis and prediction of the properties of polymer nanocomposite materials. These advanced materials, which consist of polymer matrices embedded with nanoparticles, exhibit enhanced mechanical, thermal, and electrical properties. Traditional experimental methods for characterizing these properties are often resource-intensive and time-consuming. Consequently, machine learning models have been increasingly utilized to predict the material behavior of polymer nanocomposites, offering significant improvements in efficiency, accuracy, and optimization. This paper examines various ML algorithms, including regression models, classification techniques, neural networks, and ensemble learning approaches, in the context of predicting material properties such as tensile strength, thermal stability, and electrical conductivity.