Visualization of Solar Cell Library Space by Dimensionality Reduction Methods

Visualizing high-dimensional data by projecting them into a two- or three-dimensional space is a popular approach in many scientific fields, including computer-aided drug design and cheminformatics. In contrast, dimensionality reduction techniques have been far less explored for materials informatics. Nevertheless, similar to their usefulness in analyzing the space of, e.g., drug-like molecules, such techniques could provide useful insights on materials space, including an intuitive grasp of the overall distribution of samples, the identification of interesting trends, including the formation of materials clusters and the presence of activity cliffs and outliers, and rational navigation through this space in the search for new materials. Here we present the first application of four dimensionality reduction techniques, namely, principal component analysis (PCA), kernel PCA, Isomap, and diffusion map, to visualize and analyze a part of the materials space populated by solar cells made of metal oxides. Sola...