O 2 -GEN (Figure ) encompasses a molecule generator specifically engineered for OOMs design, one (or multiple, depending on specific requirements) property selector, and a 3D pre-trained backbone network shared by both components. This architecture enables the efficient generation and property screening of OOMs. The pre-training data (Figure .a) consists of the open-source optoelectronic molecular datasets with pure condensed fused-ring molecules (COMPAS-1X , PAS ) and aromatic ring assemblies (CMR , Harvard Organic Photovoltaic Database , Ir complexes , TADF ). The detailed dataset names and their information are provided in Supplementary Table . In addition, considering that in many applications of OOMs require larger molecular weight, we developed a dataset named AOMAD (Aromatic Optoelectronic Molecular Assembly Database) through expert-guided fragment assembly (see Data part of Methods) to enhance the representativeness of molecular distributions in pre-training datasets. The pre-training dataset encompasses a total of over ten million molecules, meticulously curated to ensure the utmost diversity of OOMs.