Data‐Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top‐Down/Bottom‐Up Design

Abstract The high‐throughput exploration and screening of molecules for organic electronics involves either a ‘top‐down’ curation and mining of existing repositories, or a ‘bottom‐up’ assembly of user‐defined fragments based on known synthetic templates. Both are time‐consuming approaches requiring significant resources to compute electronic properties accurately. Here, ‘top‐down‘ is combined with ‘bottom‐up‘ through automatic assembly and statistical models, thus providing a platform for the fragment‐based discovery of organic electronic materials. This study generates a top‐down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom‐up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)‐H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top‐down/bottom‐up compounds, enabling on‐the‐fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio‐quality optical properties, this bottom‐up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton‐generating process.