  The inverse design of tailored organic molecules for specific optoelectronic
devices of high complexity holds an enormous potential, but has not yet been
realized1,2. The complexity and literally infinite diversity of conjugated
molecular structures present both, an unprecedented opportunity for
technological breakthroughs as well as an unseen optimization challenge.
Current models rely on big data which do not exist for specialized research
films. However, a hybrid computational and high throughput experimental
screening workflow allowed us to train predictive models with as little as 149
molecules. We demonstrate a unique closed-loop workflow combining high
throughput synthesis and Bayesian optimization that discovers new hole
transporting materials with tailored properties for solar cell applications. A
series of high-performance molecules were identified from minimal suggestions,
achieving up to 26.23% (certified 25.88%) power conversion efficiency in
perovskite solar cells. Our work paves the way for rapid, informed discovery in
vast molecular libraries, revolutionizing material selection for complex
devices. We believe that our approach can be generalized to other emerging
fields and indeed accelerate the development of optoelectronic semiconductor
devices in general.
