Several techniques are employed for design optimization in nanotechnology:
Computational Modeling: Simulations using tools like Density Functional Theory (DFT) and Molecular Dynamics (MD) help predict the behavior of nanomaterials and guide experimental efforts. Experimental Techniques: Techniques such as Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) are used to characterize nanomaterials and validate computational models. Machine Learning: Machine learning algorithms can analyze large datasets to identify patterns and optimize design parameters more efficiently than traditional methods.
