LAUSR

The ever-growing data acquisition speed represents a challenge for data analysis in materials sciences in general and the field of solar cells in particular. This is because many unsupervised and supervised learning algorithms require model re-derivation when presented with new samples which are markedly different from those used for model construction. Dynamic segmentation addresses this problem by continuously updating the clusters structure, for example, by splitting old clusters or opening new ones, as new samples are presented. In this work we present the application of a Dynamic Classification Unit (DCU) to the study of the photovoltaic space. Using a database of 1165 metal oxide-based solar cells, constructed from five libraries, we demonstrate that the DCU algorithm, when initiated with only 10% of the database, correctly classified 82% of the remaining, 90% samples. At the same time the algorithm unveiled the presence of interesting trends, outliers and compositional activity cliffs. These abilities may prove useful for the analysis of the photovoltaic space and in turn may contribute to the design of solar cells with improved properties. We suggest that DCU and other dynamic clustering methods will find wide applications in the rapidly developing field of materials informatics.