OPV cells have a proven efficiency of over 19 % while OPV modules have a proven record efficiency
of 13.5 %. Both values are still increasing, towards > 20 % for small area cells and > 15 % for large
scale modules. With these performance values, solution processed emerging photovoltaic
technologies are reaching out to applications that are going beyond the typical niche markets. The
first generation of commercially available printed PV modules showed a lifespan in the order of
beyond 5 years and more under outdoor conditions (OPV). Interestingly, several experiments are
strongly suggesting that solution processed semiconductors like organics can be stable under light
and, to some extent, under oxygen as well. Despite these impressive numbers, one should not forget
that these are “best you can do” lifetime values.
On the other hand, the community did not progress significantly in overcoming the fundamental limitations of OPV. The energy gap law for excitonic materials, the precise microstructure control of binary or ternary composites, the design principles for environmentally stable materials or the Kirchhoff law for multi-junction cells continue to be major barriers for this technology. We briefly introduce into these long-time challenges for excitonic absorbers and then discuss concepts and strategies how to resolve them. Among them, the development of a digital twin for OPV which has inverse predictive power is a most promising concept. “Solar FAU”, an alliance of research partners in the Erlangen-Nürnberg region that is headed by Friedrich Alexander University, is exploring the basic concepts and methodologies how to build a digital twin for emerging-PV technologies. The central and most desired element of the digital twin is the power of inverse design, e.g., inventing molecules, device architectures and processes with tailored properties. Insight from first pieces (agents) of the digital twin strongly supports the assumption that inverse design capability is possible, even in the case of considerable experimental uncertainty. Coupling the digital twin to Material Acceleration Platforms (MAP) reduces experimental uncertainty and allows to learn predictions which otherwise would be impossible. We have recently demonstrated the power of of such coupled systems and demonstrated correlations which were previously unthinkable, like the prediction of performance and lifetime of OPV cells from simple absorption data or the identification of molecular features that determine the environmental operational stability of OPV.