Metal–organic frameworks (MOFs) have become an active topic because of their excellent carbon capture and storage (CCS) properties. However, it is quite challenging to identify MOFs with superior performance within a massive combinatorial search space. To this end, we propose a deep-learning-based end-to-end prediction model to rapidly and accurately predict the CO₂ working capacity and CO₂/N₂ selectivity of a given MOF under low-pressure conditions. Different from previous methods, our prediction model relies only on the data from the Crystallographic Information File (CIF) rather than handcrafted geometric descriptors and chemical descriptors. The model was developed, trained, and tested on a dataset of 342489 topologically diverse MOFs. Experimental results on the dataset show that the proposed model achieves high prediction performance, i.e., R² = 0.916 for predicting the CO₂ working capacity and R² = 0.911 for predicting the CO₂/N₂ selectivity. With regard to the identification of potential high-performing MOFs, 1020 of 1027 (top 3%) high-performance MOFs were recovered while screening only 12% of the entire dataset using our provided pretrained model, reducing the computation time by nearly an order of magnitude when the model was used to prescreen material prior to computationally intensive grand canonical Monte Carlo (GCMC) simulations while still capturing 99% of the high-performance MOFs. In the ab initio training task, the method can achieve R² = 0.85 with only 20% of the labeled data used for training and recover 995 of 1027 (top 3%) high-performance MOFs with only 12% of the entire dataset screened.

中文翻译：

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基于深度学习的金属有机框架中二氧化碳捕获的端到端预测

金属有机框架 (MOF) 因其优异的碳捕获和储存 (CCS) 特性而成为一个活跃的话题。然而，在庞大的组合搜索空间中识别具有卓越性能的 MOF 是非常具有挑战性的。为此，我们提出了一种基于深度学习的端到端预测模型，以快速准确地预测CO ₂工作能力和CO ₂ /N ₂给定 MOF 在低压条件下的选择性。与以前的方法不同，我们的预测模型仅依赖于晶体信息文件 (CIF) 中的数据，而不是手工制作的几何描述符和化学描述符。该模型是在包含 342489 个拓扑不同的 MOF 的数据集上开发、训练和测试的。在数据集上的实验结果表明，该模型具有较高的预测性能，即预测CO ₂工作能力的R ² = 0.916，预测CO ₂ /N _2的R ² = 0.911选择性。关于潜在高性能 MOF 的识别，1027 个（前 3%）高性能 MOF 中的 1020 个被恢复，而使用我们提供的预训练模型仅筛选了整个数据集的 12%，将计算时间减少了近一个数量级当模型用于在计算密集型大规范蒙特卡罗 (GCMC) 模拟之前预筛选材料时，仍能捕获 99% 的高性能 MOF。在 ab initio 训练任务中，该方法可以实现R ² = 0.85，只有 20% 的标记数据用于训练，并在仅筛选整个数据集的 12% 的情况下恢复 1027 个（前 3%）高性能 MOF 中的 995 个。