Journal of Nanomaterials and Devices

ABSTRACT

Computational nanotechnology has transformed materials science by providing unmatched capabilities for modelling, simulating, and predicting phenomena at the nanoscale. By utilizing sophisticated computational methods such as density functional theory (DFT), molecular dynamics (MD), and machine learning (ML), this area allows for a more precise and efficient approach to designing and discovering new nanomaterials. In contrast to conventional experimental techniques, which can be labor-intensive and require significant resources, computational nanotechnology expedites the research process using tools like high-throughput screening, inverse design, and multiscale modelling. These methodologies help connect quantum-level interactions to macroscopic material characteristics, fostering the creation of next-generation materials with customized properties. In the realm of energy storage and conversion, computational techniques are essential for optimizing catalysts and enhancing battery materials to boost efficiency and performance. In nanomedicine, simulations aid in crafting targeted drug delivery systems and innovative biosensors, paving the way for personalized healthcare solutions. Likewise, in the field of electronics, computational nanotechnology plays a crucial role in developing more effective semiconductors and cutting-edge photonic devices.

KEYWORDS

1. Computational nanotechnology
2. Nanomaterials
3. Density functional theory (DFT)
4. Molecular dynamics (MD)
5. Machine learning (ML)