Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics

Abstract

Perovskite solar cells have garnered significant interest owing to their low fabrication costs and comparatively high power conversion efficiency (PCE). The performance of these cells is influenced not solely by material composition but also by experimental processes, rendering PCE prediction a challenging endeavor. It is also crucial to quantitatively assess the impact of process conditions on performance. In this work, we developed machine learning regression incorporating process information derived from an open-access perovskite database. Our analysis showed that the split of process information influenced the prediction accuracy and clarified the relative contribution of each process condition. The limitation of performance prediction was also prone to data degeneracy. The insights gained from this work may facilitate the data-driven design of innovative perovskite solar cells.