Harnessing generative AI for efficient organic materials discovery in low-data regimes

Abstract

Generative AI has emerged as a powerful tool for the discovery of organic light-emitting diode (OLED) materials. However, its practical application remains underexplored due to the small datasets and difficulties in ensuring molecular synthesizability. To overcome these challenges, we introduce a building block-based autoregressive generative model. Trained on a dataset of approximately 1000 OLED molecules, the model demonstrated refined control over key thermally activated delayed fluorescence (TADF) properties, including S₁ energy and the singlet–triplet energy gap ΔE_ST, while generating structurally novel candidates through strategic repurposing of building blocks not previously associated with TADF activity. In addition, we experimentally validated its potential by synthesizing four AI-designed green emitters and integrating them into OLED devices, achieving external quantum efficiencies of up to 11.22% at 1000 cd m⁻². It achieved more than a 100-fold reduction in the computational cost of quantum chemical calculations compared to conventional heuristic methods. This work bridges the gap between generative molecular design and experimental realization, showcasing a pathway to overcome data scarcity and unlock innovative discovery of optoelectronic materials.