Exploring materials band structure space with unsupervised machine learning

An unsupervised machine learning algorithm is applied for the first time to explore the space of materials electronic band structures. T-student stochastic neighbor embedding (t-SNE), a state of the art algorithm for visualization of high dimensional data, is applied on feature spaces constructed by extracting electronic fingerprints straight from Brillouin zone of the materials. Different spaces are designed and mapped to lower dimensions allowing to analyze and explore this previously uncharted band structure space for thousands of materials at once. In all cases analyzed machine learning was able to learn and cluster the materials depending on the features involved. t-SNE promises to be a extremely useful tool for exploring the materials space.