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...

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Bibliographic Details
Published in:Nature communications 2021-04, Vol.12 (1), p.2422-2422, Article 2422
Main Authors: Kunkel, Christian, Margraf, Johannes T., Chen, Ke, Oberhofer, Harald, Reuter, Karsten
Format: Article
Language:English
Subjects:
639/301/923/3931
639/638/563/606
Charge injection
Computer applications
Design
Electronics industry
First principles
Humanities and Social Sciences
Learning algorithms
Machine learning
Morphing
multidisciplinary
Organic chemistry
Organic semiconductors
Science
Science (multidisciplinary)
Search methods
Semiconductors
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Summary: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.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-22611-4