Abstract

Computational materials science, combining artificial intelligence (AI) and advanced simulations, is reshaping the discovery of nanomaterials with unprecedented precision and efficiency. AI techniques such as machine learning and deep learning are being integrated with computational tools, enabling the prediction of material properties and behavior at the nanoscale. This interdisciplinary approach accelerates the identification of promising nanomaterials by reducing the need for extensive experimental trials. Computational methods such as density functional theory (DFT) and molecular dynamics (MD) simulations allow for the modeling of atomic-level interactions, providing insights into material design. By leveraging large datasets and predictive algorithms, AI can identify relationships between material structures and their performance characteristics, enabling faster innovation in fields like electronics, energy storage, and catalysis. Despite challenges such as data quality and model generalization, computational materials science is advancing rapidly, offering a new paradigm for sustainable and efficient material discovery and optimization.