Fast screening of performance and stability of organic solar cells is made through the use of thickness and thermal gradients, as well as different deposition temperatures and solvents.

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

Blade coating in combination with non-toxic solvents provides high photovoltaic efficiency in perovskite thin films by spherulitic growth crystallization.

High-throughput experimental screening and machine-learning algorithms are implemented in a synergic workflow to predict the photocurrent phase space of organic photovoltaic blends. We identify accurate models employing only the materials band gaps.

We use state-of-the-art absorbing materials and industrially compatible processing techniques and conditions to fabricate semitransparent organic photovoltaic (OPV) module prototypes that exceed 30% transparency.