Graph neural network-based multi-objective Bayesian optimization for enhanced screening of metal–organic frameworks with optimal separation performance

Key Points

• The study aims to improve the screening process of metal-organic frameworks (MOFs) for better separation performance using advanced optimization methods.
• Utilized graph neural networks for modeling the properties of MOFs.
• Implemented multi-objective Bayesian optimization to explore optimal structural features.
• Focused on enhancing separation performance in the screening process.
• Identified several MOFs with superior separation capabilities.
• Achieved higher efficiency in the screening process compared to traditional methods.
• Demonstrated the potential for practical applications in gas capture and drug delivery.