Innovative Vision: Exploring Artificial Intelligence and Machine Learning for Advancing Polymer Nanocomposites

This study utilizes machine learning and artificial intelligence to unravel the complexities of polymer nanocomposites reinforced with silver-coated gold nanoparticles (AgC NPs). Through an extensive dataset derived from tensile tests, we explore the mechanical behaviour of these nanocomposites, focusing on their elastic and plastic responses up to failure. The dataset encompasses critical metrics such as elastic modulus, yield point, maximum force, and break force, alongside deformation measurements collected over the test duration. Employing advanced data analytics, we identify patterns and relationships within the data, revealing how adding AgC NPs enhances the material properties. Machine learning models are trained to predict mechanical performance based on nanoparticle concentration, providing insights into the optimal composition for desired properties. The AI-driven approach accelerates the analysis process and offers a deeper understanding of the material behaviour, paving the way for developing nanocomposites with adapted mechanical characteristics. This research exemplifies the synergy between materials science and computational technology, showcasing how AI and machine learning can revolutionize the study of material properties and their optimization for various applications.