Improving charge carrier mobilities of organic semiconductors is usually tackled by empirical structural tuning of a few promising compounds. Knowledge-based methods can greatly accelerate such local exploration by providing an overview of the problem-specific design space. Here, we provide such an overview for organic semiconductors, applying data mining strategies to an in-house database of &gt;64.000 organic molecular crystals, annotated with charge-transport descriptors (electronic coupling and the reorganization energy) that are calculated from first principles. This database-screening recovers many known and well- performing materials, while also uncovering many more promising candidates, not yet considered for organic electronics applications. Analysing the design space regions in this dataset by a chemical space network hints at already explored or promising regions. We further derive general design principles by evaluating the performance of molecular scaffold and sidegroup clusters of compounds. For these, we find certain scaffolds (sidegroups) to consistently improve charge-transport properties. Functionalizing promising scaffolds with favorable sidegroups then results in molecular crystals with improved charge-transport properties, highlighting the usefulness of data-based approaches for a targeted design of organic electronics materials.

Finding the right building blocks for molecular optimization: Mining a database of organic semiconductors

2019 Fall National Meeting

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.