Multimodal Transformer for Sample-Aware Prediction of Metal-Organic Framework Properties
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 introduce Experimental X-ray Diffraction Integrated Transformer (EXIT), a multimodal transformer for sample-aware prediction of MOF properties that combines MOFid with X-ray diffraction (XRD). In EXIT, MOFid encodes MOF identity, whereas XRD provides compleme...
| Authors | Seunghee Han, Jaewoong Lee, Jihan Kim |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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