Scientific Sessions

AI in nanotechnology is reshaping research and development by accelerating materials discovery, optimizing manufacturing processes, and enabling precise control at the nanoscale. Machine learning algorithms analyze vast datasets to predict material properties, design new nanomaterials, and optimize experimental conditions. This synergy enhances the efficiency of nanotechnology research by reducing trial and error and uncovering novel materials with tailored properties for specific applications. In manufacturing, AI-driven automation improves the precision and scalability of nanofabrication techniques such as lithography and 3D printing, facilitating the production of nanoscale devices and structures. Applications span diverse fields, including electronics, healthcare, energy, and environmental sustainability, where AI enhances device performance, diagnostic accuracy, and resource efficiency. Challenges include data reliability, interpretability of AI models, and ethical considerations in AI-driven decision-making. Ongoing research focuses on integrating AI with experimental techniques, developing AI-powered simulations for complex nanosystems, and addressing societal impacts of AI-enabled nanotechnology.