用多模态融合预测COFs气体吸附性能，无需依赖具体气体参数。
COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy
- 通过深度学习提取结构与化学多模态特征，融合交叉注意力机制。
- 在hypoCOFs数据集上达到顶尖性能，无需气体特异性描述符。
- 发现高性能COFs集中在特定孔径和比表面积范围，适合材料筛选研究。
共价有机框架（COFs）是极具潜力的气体吸附与分离材料，但其庞大设计空间中识别最优结构需高效高通量筛选。传统机器学习方法高度依赖特定气体相关特征，这类特征获取耗时且限制可扩展性，导致效率低下与人工成本高。本文提出通用的COFs吸附预测框架COFAP，通过深度学习自动提取多模态结构与化学特征，并利用交叉模态注意力机制进行特征融合。COFAP无需显式依赖气体特异性热力学描述符，在本研究考察条件下于hypoCOFs数据集上实现了当前最佳预测性能，优于已有方法。基于COFAP，我们发现高性能用于气体分离的COFs集中于狭窄的孔径与比表面积范围。此外，开发了可调权重的优先级排序方案，支持研究人员按应用场景灵活筛选候选材料。卓越的效率与准确性使COFAP可直接部署于晶体多孔材料研究。
原文摘要 · Abstract (English)
Covalent organic frameworks (COFs) are promising adsorbents for gas adsorption and separation, while identifying the optimal structures among their vast design space requires efficient high-throughput screening. Conventional machine-learning predictors rely heavily on specific gas-related features. However, these features are time-consuming and limit scalability, leading to inefficiency and labor-intensive processes. Herein, a universal COFs adsorption prediction framework (COFAP) is proposed, which can extract multi-modal structural and chemical features through deep learning, and fuse these complementary features via cross-modal attention mechanism. Without relying on explicit gas-specific thermodynamic descriptors, COFAP achieves state-of-the-art prediction performance on the hypoCOFs dataset under the conditions investigated in this study, outperforming existing approaches. Based on COFAP, we also found that high-performing COFs for gas separation concentrate within a narrow range of pore size and surface area. A weight-adjustable prioritization scheme is also developed to enable flexible, application-specific ranking of candidate COFs for researchers. Superior efficiency and accuracy render COFAP directly deployable in crystalline porous materials.
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