Mathematical Physics Approaches to Nanotechnology and Material Science

This work examines the use of mathematical physics tools and artificial intelligence in enhancing nanotechnology and materials science studies. Using AI algorithms interacting with the basic physical models the paper investigates how these combined methods may enhance the design and characteristics of nanomaterials. Support Vector Machine (SVM), Artificial Neural Networks (ANN), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed, whereby predictions of material properties were made, synthesis processes optimized and characterization methods improved. As a result, the authors reported that AI-based models ‘ raised the bars’ in the prediction of material properties with respective accuracies of 92% SVM and 89% ANN. Based on optimization, result showed that the GA algorithm provided 35% improvement in material while the PSO provided increased energy efficiency of 40% on nanomaterial application. In experimental research outcomes, the efficiency of AI technique also proved that AI methodologies were 25% faster process than synthesis process and cost 20% less compared to standard methodologies. These outcomes show that AI and mathematical physics are versatile tools to transform material synthesis and advance the search for environment-friendly, high-performance materials at a much faster pace. This study reveals that other fields such as AI and physics can collaborate to enhance developments in the nanotechnology and materials science fields.