The consequence of unpaired electrons in organic molecules has fascinated and confounded chemists for over a century. The study of open-shell molecules has been rekindled in recent years as new synthetic methods, improved spectroscopic techniques and powerful computational tools have been brought to bear on this field. Nonetheless, it is the intrinsic instability of the biradical species that limits the practicality of this research. Here we report the synthesis and characterization of a molecule based on the diindeno[b,i]anthracene framework that exhibits pronounced open-shell character yet possesses remarkable stability. The synthetic route is rapid, efficient and possible on the gram scale. The molecular structure was confirmed through single-crystal X-ray diffraction. From variable-temperature Raman spectroscopy and magnetic susceptibility measurements a thermally accessible triplet excited state was found. Organic field-effect transistor device data show an ambipolar performance with balanced electron and hole mobilities. Our results demonstrate the rational design and synthesis of an air- and temperature-stable biradical compound.

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as...

The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus­(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization.

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