Metal-organic frameworks (MOFs) are prime candidate materials for gas adsorption and separation owing to their exceptional porosity and structural tunability. However, the nearly infinite chemical space and exponentially growing number of candidate structures pose insurmountable challenges to traditional experimental methods and brute-force computational screening. Data-driven machine learning (ML) offers a transformative solution for efficiently navigating this vast materials library. This review analyzes the current state of ML-based MOF screening, evaluates the limitations of mainstream MOF databases, and highlights how data authenticity and update frequency affect model reliability. The evolution of feature engineering─from manual geometric descriptors to automated representation learning using graph neural networks (GNNs) and molecular fingerprints─is also outlined. Furthermore, we discuss the specific applicability of advanced algorithmic frameworks, including deep learning, active learning, and transformers, to MOF screening tasks. Future development should focus on integrating high-fidelity experimental data with model interpretability to enable closed-loop autonomous discovery systems.
Zhang et al. (Sun,) studied this question.