Active discovery of organic semiconductors

Abstract

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.

Virtual Screening for Organic Solar Cells and Light Emitting Diodes

“…[ 131 ] This process can be further optimized by using active machine learning [ 132 , 133 ] or generative models. [ 134 ]…”

High-throughput virtual screening for organic electronics: a comparative study of alternative strategies

“… 441,442 Other recent studies have focused on how improving the ML-guided exploration of materials space, and for example active machine learning has proven able to identify OSCs with larger charge conduction properties than competing exploration strategies. 443 …”

Light-Induced Organic Transformations by Covalent Organic Frameworks as Reticular Platforms for Selective Photosynthesis

“…As a matter of fact, the ideal COF-based photocatalyst, tailored for a specific reaction, might be already described in the literature, in terms of its molecular composition and framework arrangement. In this context, the emerging power of active machine learning (AML) approaches − will probably be of great help in assisting and accelerating the discovery of the best suitable COF-based photocatalyst, for instance by analyzing charge transport, optical absorption range, and linkage bond strength, among other specific descriptors. Indeed, fundamental photophysical studies can provide important guidelines for photocatalytic COF design.…”