Electrical engineering

Physics

Nanotechnology

Funnel

Bioinformatics

Biology

Organic semiconductor

Chemical space

Operating system

Engineering

Electronics

Artificial intelligence

Biochemical engineering

Materials science

Space (punctuation)

Quality (philosophy)

Computer science

Quantum mechanics

Drug discovery

Optoelectronics

Morphing

Mechanical engineering

Nature Communications

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.

Active discovery of organic semiconductors