Data driven models in nanotechnology leverage extensive datasets to understand, predict, and optimize nanoscale phenomena. These models utilize machine learning, artificial intelligence, and statistical techniques to analyze data from experimental and theoretical sources. The goal is to identify patterns, correlations, and causal relationships that can inform the design and application of nanomaterials and nanodevices.
