Nanomaterials often involve large datasets that are complex and high-dimensional. Utilizing autoencoders can significantly reduce the dimensionality of these datasets, making them easier to analyze and visualize. This is particularly useful for researchers working on projects like nanoparticle synthesis and nanocomposites, where understanding the underlying patterns and anomalies is crucial.
