Active discovery of organic semiconductors

The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space. Existing methods for organic semiconductor computational screening are limited by the computational demand of the process, leading to the identification of non-optimal material candidates. Here, the authors report machine learning method to guide the discovery of organic semiconductors.

Automated summary

The versatility of organic molecules allows for a rich design space in organic semiconductors, which requires efficient search strategies. The active machine learning approach presented in this study rapidly identifies molecular OSC candidates with superior charge conduction properties, outperforming conventional computational methods. The method offers deep methodological insight and constantly discovers new candidates with high efficiency in the endless design space.