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

2026-04-21

AI Summary

Existing models for predicting metal–organic framework (MOF) properties rely solely on framework topology, neglecting sample-level variations such as crystallinity and defects, which limits prediction accuracy. This work proposes EXIT, a novel multimodal Transformer architecture that integrates MOFid molecular identifiers with experimental X-ray diffraction (XRD) patterns. Pretrained on million-scale simulated data and fine-tuned on real experimental measurements, EXIT bridges the gap from framework-aware to sample-aware property prediction. By leveraging attention mechanisms, the model elucidates how XRD features influence property variations, achieving state-of-the-art performance in predicting surface area and pore volume. Notably, EXIT delivers high-precision, differentiated predictions for samples sharing identical MOF identities but exhibiting distinct XRD characteristics.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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 complementary information about the experimentally realized sample state. EXIT is pre-trained on one million hypothetical MOFs with simulated XRD to learn transferable representations, leading to improved downstream performance relative to existing approaches. EXIT is fine-tuned on literature-derived experimental datasets for surface area and pore volume prediction. Incorporating experimental XRD improves predictive performance relative to models without experimental XRD, and attention analysis and sample-level case studies further show that EXIT assigns different predictions to samples sharing the same MOF identity when their XRD patterns differ. These results establish a practical step from framework-aware to sample-aware MOF property prediction and highlight the value of incorporating experimental characterization into porous materials informatics.

Problem

• metal-organic frameworks
• sample-aware prediction
• property prediction
• X-ray diffraction
• experimental variability

Innovation

• multimodal transformer
• sample-aware prediction
• X-ray diffraction (XRD)
• MOF property prediction
• transfer learning