Abstract
This work demonstrates an active learning (AL) workflow for identifying promising material candidates for organic lightemitting diodes (OLEDs) based on multiple optoelectronic parameters while minimizing the number of physics‐based computations to explore an extensive library. This work paves the way for efficient computational materials screening before laborious synthesis, and device fabrication.
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