Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties

ArXi:2604.19383v1 Announce Type: cross Metal-organic frameworks (MOFs) are a major target of machine-learning-based property prediction, yet most models assume that a single framework representation maps to a single property value. This assumption becomes problematic for experimental MOFs, where samples reported as the same framework can exhibit different properties because of differences in crystallinity, phase purity, defects, and other sample-dependent factors. Here we