AI-Driven Nanotechnology: Transforming Materials Science, Medicine, and Electronics

Artificial intelligence (AI) is transforming nanotechnology by processing large datasets, modeling nanoscale interactions, and optimizing experimental and manufacturing processes, thereby advancing materials science, nanomedicine, nanoelectronics, and environmental applications. AI-driven machine learning enhances the characterization and classification of nanomaterials, improves image analysis in atomic force and scanning probe microscopy, and accelerates the discovery of novel nanostructures with tailored properties. In nanomedicine, AI supports the optimization of nano-structured biomaterials for diagnostics and treatments, the prediction of nanotoxicity, and the design of nanoparticles for targeted drug delivery, while in nanoelectronics it enables the development of advanced nanocomputing architectures, optimizing device design and exploring quantum computing applications. Additionally, AI contributes to environmental nanotechnology by simulating nanoparticle interactions with biological and ecological systems, leading to the development of safer, more effective nanomaterials. However, challenges such as the need for interpretable models aligned with experiments, computational complexity, and data standardization persist, along with ethical concerns regarding regulatory compliance and nanotoxicity evaluation. Future research must focus on building robust AI frameworks that bridge theoretical models with experimental validation, foster interdisciplinary collaboration, and ensure the ethical use of AI in nanoscience, highlighting its vast potential to drive scientific progress and address pressing global challenges.