Inverse interface design for optimal organic photovoltaics

• Applicants: Dr. Denis Andrienko; Professor Dr. Christoph J. Brabec; Professor Dr. Dieter Neher; Professorin Dr. Safa Shoaee
• Subject Area: Experimental Condensed Matter Physics
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 460766640

EXTRAORDINAIRE II will use inverse design for organic photovoltaics (OPV) to discover novel non-fullerene electron acceptor (NFA) molecules forming optimal interfaces with high-performance electron donor polymers with the potential for power conversion efficiencies (PCE) beyond 20%. The project builds on a first funding period, in which decisive material properties have been identified that must be controlled in order to further increase the performance of donor:acceptor (D:A) blends in OPV devices. These are: the energy and lifetime of the triplet state, the reorganization energy for exciton dissociation at the D:A interface, and the energetic of the blend at and near the D:A interface to warrant efficient charge separation. In this follow-up project, we will identify and quantify the link between molecular motifs in NFA molecules and each of these decisive material parameters, such that optimal interfacial properties of D:A blends can be directly predicted from molecular structure. This will enable inverse design, an artificial intelligence (AI) driven active learning scheme, with the goal of discovering NFA molecules optimizing all of the decisive material properties at the same time. The goal requires a dense interaction in an interdisciplinary consortium comprising experts in high throughput organic synthesis and device formulation, photophysical characterization and modeling as well as quantum chemical calculations. We will synthesize a large number of NFA molecules and perform AI guided morphology optimization and high throughput optoelectronic characterization, guided by predictions from quantum chemical calculations. Selected samples with most promising results will undergo detailed (high definition) photophysical characterization across all relevant time and energy scales. In the same samples, the properties at and close to the D:A interface will be modelled by advanced quantum chemical calculations. Consistency between experimental and simulation data will be checked through an analytical 5 level model including triplet state formation and decay. Both high definition and high throughput results will be merged into a mixed fidelity machine learning model which will thus be able to learn the link between molecular structure and detailed optoelectronic functionality. According to the model’s suggestions, and the domain knowledge present in the consortium, further molecules will be synthesized and the cycle will be repeated. This will improve the predictive capacity of the model towards the final goal. It is expected that an optimization strategy, guided by the crucial device physics rather than just the final device performance, leads to a significant acceleration of molecular discovery towards optimal D:A blends for the OPV technology.

• DFG Programme: Research Grants
• Co-Investigator: Dr. Larry Lüer