NANOTECHNOLOGY EMPOWERED BY ARTIFICIAL INTELLIGENCE: A STATE-OF-THE-ART REVIEW ON MATERIAL SYNTHESIS AND CHARACTERIZATION

The convergence of artificial intelligence (AI) and nanotechnology is an emergent paradigm of materials science, especially with regards to the synthesis and characterization of materials. This review clarifies the fast-growing synergy of AI algorithms including machine learning (ML) model or deep neural networks, are accelerating the discovery and optimization of nanomaterials more than ever before. Synthesizing AI-based methods, including generative adversarial networks and reinforcement learning, allow the logical programming of the nanostructures so that experiments take days instead of years. Properties, such as conductivity, biocompatibility, and catalytic activity, can be engineered. As an example, carbon-based nanomaterials and metal-organic structures have been simplified through data-driven platforms and can be scaled up to be applicable in the industry. Under characterization, AI has demonstrated itself to excel in the processing of large datasets based on such techniques as X-ray diffraction, Raman spectroscopy, and electron microscopy, and uses convolutional neural networks to automatically detect defects, identify phases, and predict properties with over 95% accuracy. This synthesis does not only mitigate human biases but also reveals concealed correlations in complicated nanoscale processes, paving the way to breakthroughs in energy storage, nanomedicine and environmental remediation. Alongside these developments, there are still problems such as the lack of data and the possibility of interpreting AI-driven experiments and ethical issues. In the future, AI-quantum computing systems will be optimistic and will transform the operation of molecular simulations, leading to sustainable, next-generation nanomaterials. Key words: Artificial Intelligence, Nanotechnology, Material Synthesis, Characterization Techniques, Machine Learning.