Machine learning (ML) techniques are increasingly used in nanotechnology for tasks such as material discovery, property prediction, and process optimization. ML algorithms can analyze large datasets to identify patterns and relationships that are not easily discernible by traditional methods. For instance, supervised learning can be used to predict the properties of new nanomaterials based on known data, while unsupervised learning can help in clustering and classifying different nanostructures.
