COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy

A novel framework named COFAP has been introduced to predict the adsorption capabilities of covalent organic frameworks (COFs), addressing challenges in identifying optimal structures efficiently. Unlike traditional machine learning methods that depend heavily on specific gas-related features, COFAP employs a designed multi-modal extraction technique combined with cross-modal synergy to enhance prediction accuracy. This approach aims to streamline the process, reducing the inefficiencies and time consumption associated with prior methods. The framework’s effectiveness has been positively proposed, suggesting it could significantly improve adsorption prediction in the domain of COFs. By leveraging multi-modal data, COFAP overcomes limitations inherent in conventional models, potentially accelerating material discovery and optimization. This development reflects ongoing efforts to integrate advanced machine learning strategies within materials science, particularly for applications involving COFs adsorption.