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The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential but has not yet been realized. Current models rely on large data sets that generally do not exist for specialized research fields. We demonstrate a closed-loop workflow that combines high-throughput synthesis of organic semiconductors to create large data sets and Bayesian optimization to discover new hole-transporting materials with tailored properties for solar cell applications. The predictive models were based on molecular descriptors that allowed us to link the structure of these materials to their performance. A series of high-performance molecules were identified from minimal suggestions and achieved up to 26.2% (certified 25.9%) power conversion efficiency in perovskite solar cells.
That milestone underlines the feasibility of developing autonomous research strategies that discover materials tailored for specific applications. That requires a highly interconnected workflow including synthesis, purification, characterization and device optimization. Such lines could specifically develop optimized interface materials for perovskite cells with various bandgaps, but also discover optimized interfaces for LEDs, photodetectors or X-Ray detectors. The outlook will summarize the advantages but also the limitations of data driven methods and will give further examples of such campaigns searching to find optimized materials for very different applications.