FAIR Modeling for Perovskite Solar Cells: An Open Source Machine Learning Pipeline

Abstract

Perovskite solar cells (PSCs) show great promise for commercialization, rivaling traditional silicon-crystal solar cell efficiency despite their comparatively short research lifetime. This efficiency is achieved while being manufactured at low temperatures and in ambient conditions, lowering fabrication costs dramatically. Machine learning (ML) promises to significantly expedite further optimization by recommending novel configurations based on insight from existing literature. This paper utilizes the Perovskite Database Project (PDP), an open source PSC database consisting of over 43,000 entries from published literature, to train three ML architectures with short circuit current density (J$_{sc}$) as a target. Using the XGBoost architecture, an RMSE of 3.73 $\frac{mA}{cm^2}$, R-value of 0.63, and MPE of 10.35% were achieved. This performance is comparable to the results reported in literature and through further investigation could likely be improved. To overcome the challenges of manual database creation, an open-sourced data cleaning-pipeline was created to leverage the PDP. Through the creation of these tools this research aims to increase the availability of ML as a tool to promote improvement in novel device configurations for PSC while showing the already promising performance achieved.