Computational Approaches in Nanomaterials: A Review of DFT and Machine Learning Applications

Nanomaterials have become crucial components in contemporary technologies, including nano-electronics, energy storage solutions, and biomedicine. Despite their importance, the wide variety in their composition and structure poses significant challenges for both experimental analysis and theoretical study. Density Functional Theory (DFT) has established itself as one of the most dependable quantum-mechanical methods for examining atomic-scale phenomena and forecasting essential material properties. This review compiles the latest advancements in utilising DFT to comprehend the electronic, structural, and catalytic characteristics of a broad range of nanomaterials. Furthermore, the article explores how the swift integration of Machine Learning (ML) with DFT is revolutionising the speed of materials discovery. By deriving correlations from datasets produced by DFT, ML models can predict essential material properties—such as band gaps, adsorption energies, and catalytic reaction pathways—with impressive precision and significantly reduced computational expense. The review also emphasises the development of new hybrid frameworks, such as machine learning-assisted interatomic potentials, graph-based property prediction, and generative AI-driven material design. It identifies challenges and future research directions, including model interpretability, data reliability, and the application to more complex material systems. In summary, the integration of DFT and ML is creating a robust and scalable approach for the rational design of nanomaterials.