Applications of artificial intelligence in nanotechnology

• Published in: Engineering and Technology Journal Vol. 42; no. 9; pp. 1193 - 1209
• Main Authors: Shahad Eabd Alrida, Ola Obed, Elaf Taha, Thamer Abdullah, Mustafa Hathal, Viola Somogyi
• Format: Journal Article
• Language: English
• Published: Unviversity of Technology- Iraq 12-09-2024
• Subjects: artificial intelligence; artificial neural networks; machine learning; molecular design; nanocomposites; nanomaterials; nanotechnology
• ISSN: 1681-6900; 2412-0758
• DOI: 10.30684/etj.2024.148957.1736

Artificial intelligence (AI) is emerging as a prominent technological advancement. It is the act of replicating human intelligence for many purposes. In contrast to conventional methodologies, artificial intelligence (AI) is undergoing tremendous advancements. The present state of artificial intelligence (AI) technology enables them to effectively address numerous intricate difficulties with proficiency comparable to a human's. The significance of advancements in AI is particularly evident in machine learning, where the techniques and algorithms are effectively applied to address many problems, including those in nanotechnology. In contemporary nanotechnology, it is crucial to expedite the search for the most favorable synthesis parameters while developing novel nanomaterials. The convergence of machine learning and nanotechnology necessitates a comprehensive examination of existing data on the application of artificial intelligence (AI) in addressing challenges in the nanomaterials science field. This review should encompass various stages, including computer design, chemical synthesis, and diagnostics of the resultant nanomaterials. Significant emphasis is placed on employing machine-learning technologies to investigate the thermal and dynamic characteristics of nanofluids, the sorption processes of nanocomposites, the catalytic activity of nanoparticles, and the toxicity of nanoparticles. Additionally, these technologies are utilized to address nanosensor issues and process experimental data acquired during the diagnostics of different nanomaterial properties.