Analyzing research data in nanotechnology involves a combination of experimental and computational methods. Data analysis often requires statistical techniques to interpret large datasets and identify trends. Computational tools such as molecular dynamics simulations and density functional theory (DFT) are used to model nanoscale interactions and predict the properties of nanomaterials. Additionally, machine learning algorithms are increasingly being employed to analyze complex datasets and accelerate discovery.
