Visualization Based Data Mining for Comparison Between Two Solar Cell Libraries

Material informatics may provide meaningful insights and powerful predictions for the development of new and efficient Metal Oxide (MO) based solar cells. The main objective of this paper is to establish the usefulness of data reduction and visualization methods for analyzing data sets emerging from multiple all-MOs solar cell libraries. For this purpose, two libraries, TiO₂ |Co₃ O₄ and TiO₂ |Co₃ O₄ |MoO₃ , differing only by the presence of a MoO₃ layer in the latter were analyzed with Principal Component Analysis and Self-Organizing Maps. Both analyses suggest that the addition of the MoO₃ layer to the TiO₂ |Co₃ O₄ library has affected the overall photovoltaic (PV) activity profile of the solar cells making the two libraries clearly distinguishable from one another. Furthermore, while MoO₃ had an overall favorable effect on PV parameters, a sub-population of cells was identified which were either indifferent to its presence or even demonstrated a reduction in several parameters.