How is Unsupervised Learning Applied in Nanotechnology?
In nanotechnology, unsupervised learning can be particularly useful due to the vast amount of data generated from experiments and simulations. Applications include:
Clustering: Grouping nanoparticles based on their properties like size, shape, or chemical composition. Dimensionality Reduction: Simplifying datasets with many variables while preserving their core information, which is crucial for visualizing high-dimensional nanomaterial data. Anomaly Detection: Identifying unusual patterns or anomalies in nanomaterial synthesis or behavior, which could indicate defects or novel phenomena.
