Nanomaterials, which range in size from 1 to 100 nm, have special physical, chemical, and electrical characteristics that make them extremely promising for use in environmental technologies, electronics, energy, and medicine. The discovery and development of nanomaterials has been greatly expedited in recent years by the integration of artificial intelligence (AI) and sophisticated computer techniques. This review examines how machine learning (ML) and artificial intelligence techniques, along with computational methods like density functional theory (DFT), Monte Carlo (MC), and molecular dynamics (MD) simulations, can be used to predict material properties and optimize synthesis processes. Research indicates that when compared to conventional trial-and-error methods, AI-assisted models can improve prediction accuracy for nanomaterial attributes while cutting computational screening time by up to 60-80%. Furthermore, faster identification of ideal synthesis conditions and material architectures is made possible by AI-driven automation and robotic experimentation platforms. Notwithstanding these developments, problems with toxicity assessment, environmental effect assessment, and AI model interpretability still exist. All things considered, the combination of AI and computational modeling offers a strong foundation for boosting nanomaterial innovation and aiding in the creation of safer and more effective materials for upcoming technological uses.
Mimona et al. (Fri,) studied this question.