Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials

Organic optoelectronic materials (OOMs) are pivotal for advancing technologies such as organic photovoltaics and light-emitting diodes. Traditional methods for discovering new OOMs are inefficient and limited by chemical space exploration. We introduce O2-GEN, a novel framework leveraging a 3D pretraining backbone trained on a diverse data set of over ten million molecules, enabling comprehensive exploration of chemical space. O2-GEN is effective at generating novel fused-ring systems and conjugated fragment assemblies. It significantly outperforms existing models in these specific tasks in speed and chemical structural validity, particularly for larger molecules. The framework supports both global and local generation modes, allowing for the design of new molecules or modifications of existing molecules. Additionally, O2-GEN integrates a property selector fine-tuned with density functional theory data, enabling precise multiproperty screening. Overall, our O2-GEN allows the construction of data sets with multiproperty-biased distributions tailored to specific application scenarios, facilitating the discovery of novel optoelectronic materials.