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

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 that require significant resources to compute electronic properties accurately. Here, we combine 'top-down' with 'bottom-up' through automatic assembly and statistical models and provide a platform for the fragment-based discovery of organic electronic materials. We generate a top-down set of almost 117K synthesized molecules containing their structures, electronic and topological properties and chemical composition, and use them as a building block library for bottom-up fragment-based design. A tool is developed to automate the coupling of these building blocks based on their available C(sp2/sp)-H bonds, providing a fundamental link between the two philosophies of dataset construction. Statistical models are trained on this dataset and a subset of the resulting hybrid top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties (frontier molecular orbital gaps, excitation energies) with high accuracy across known p-block organic compound space. With access to ab initio-quality optical properties, it is possible to apply this bottom-up pipeline using existing compounds as building blocks to any materials design campaign. To illustrate this, we screen over a million molecular candidates for singlet fission, the leading candidates of which provide insight into the structural features promoting this multiexciton-generating process. This article is protected by copyright. All rights reserved.