Yue Yifei, Palakkal Athulya S, Mohamed Saad Aldin, Jiang Jianwen
Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapore.
Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore 119077, Singapore.
ACS Appl Mater Interfaces. 2024 Oct 30;16(43):58754-58763. doi: 10.1021/acsami.4c14983. Epub 2024 Oct 21.
Metal-organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, a handful of MOFs have been explored for the storage of toxic fluorinated gases (Keasler et al. 1455), yet the potential of a great number of MOFs for such an environmentally sustainable application has not been thoroughly investigated. In this work, we apply active learning (AL) to accelerate the discovery of hypothetical MOFs (hMOFs) that can efficiently store a specific fluorinated gas, namely, vinylidene fluoride (VDF). First, a force field was developed for VDF and utilized to predict the working capacities () of VDF in an initial data set of 4502 MOFs from the computation-ready experimental MOF (CoRE-MOF) database that successfully underwent featurization and grand-canonical Monte Carlo simulations. Next, the initial data set was diversified by Greedy sampling in an unexplored sample space of 119,387 hMOFs from the ab initio REPEAT charge MOF (ARC-MOF) database. A budget of 10,000 samples (i.e., <10% of total ARC-MOFs) was selected to train a random forest model. Then, in the unlabeled ARC-MOFs were predicted and top-performing ones were validated by simulations. Integrating with the stability requirement, mechanically stable ARC-MOFs were finally identified, along with high . Furthermore, by Pareto-Frontier analysis, we revealed that long linear linkers can enhance , while bulkier multiphenyl linkers or interpenetrated frameworks improve mechanical strength. From this work, we efficiently discover top-performing MOFs for VDF storage by AL and also demonstrate the importance of integrating stability to identify stable promising MOFs for a practical application.
金属有机框架（MOFs）是用于各种重要应用的多功能纳米多孔材料。最近，已经探索了少数几种MOFs用于储存有毒的氟化气体（Keasler等人，1455），然而，大量MOFs在这种环境可持续应用方面的潜力尚未得到充分研究。在这项工作中，我们应用主动学习（AL）来加速发现能够有效储存特定氟化气体即偏二氟乙烯（VDF）的假设MOFs（hMOFs）。首先，为VDF开发了一个力场，并用于预测来自计算就绪实验MOF（CoRE-MOF）数据库的4502个MOFs初始数据集中VDF的工作容量（），该数据库成功进行了特征化和巨正则蒙特卡罗模拟。接下来，通过贪婪采样在来自从头算重复电荷MOF（ARC-MOF）数据库的119387个hMOFs未探索样本空间中对初始数据集进行多样化处理。选择10000个样本的预算（即占ARC-MOF总数的不到10%）来训练随机森林模型。然后，对未标记的ARC-MOFs进行预测，并通过模拟验证表现最佳的MOFs。结合稳定性要求，最终确定了机械稳定的ARC-MOFs以及高的。此外，通过帕累托前沿分析，我们发现长线性连接体可以提高，而体积更大的多苯基连接体或互穿框架可以提高机械强度。通过这项工作，我们通过主动学习有效地发现了用于VDF储存的表现最佳的MOFs，并且还证明了整合稳定性以识别用于实际应用的稳定且有前景的MOFs的重要性。
