Metal organic frameworks (MOFs) are crystalline, three-dimensional structures with high surface areas and tunable porosities. Made from metal nodes connected by organic linkers, the exact properties of a given MOF are determined by node and linker choice. MOFs hold promise for numerous applications, including gas capture and storage. M2(4,4′-dioxidobiphenyl-3,3′-dicarboxylate)—henceforth simply M2(dobpdc), with M = Mg, Mn, Fe, Co, Ni, Cu, or Zn—is regarded as one of the most promising structures for CO2 capture applications. Further modification of the MOF with diamines or tetramines can significantly boost gas species selectivity, a necessity for the ultra-dilute CO2 concentrations in the direct-air capture of CO2. There are countless potential diamines and tetramines, paving the way for a vast number of potential sorbents to be probed for CO2 adsorption properties. The number of amines and their configuration in the MOF pore are key drivers of CO2 adsorption capacity and kinetics, and so a validation of computational prediction of these quantities is required to suitably use computational methods in the discovery and screening of amine-functionalized sorbents. In this work, we study the predictive accuracy of density functional theory and related calculations on amine loading and configuration for one diamine and two tetramines. In particular, we explore the Perdew–Burke–Ernzerhof (PBE) functional and its formulation for solids (PBEsol) with and without the Grimme-D2 and Grimme-D3 pairwise corrections (PBE+D2/3 and PBEsol+D2/3), two revised PBE functionals with the Grimme-D2 and Grimme-D3 pairwise corrections (RPBE+D2/3 and revPBE+D2/3), and the nonlocal van der Waals correlation (vdW-DF2) functional. We also investigate a universal graph deep learning interatomic potential’s (M3GNet) predictive accuracy for loading and configuration. These results allow us to identify a useful screening procedure for configuration prediction that has a coarse component for quick evaluation and a higher accuracy component for detailed analysis. Our general observation is that the neural network-based potential can be used as a high-level and rapid screening tool, whereas PBEsol+D3 gives a completely qualitatively predictive picture across all systems studied, and can thus be used for high accuracy motif predictions. We close by briefly exploring the predictions of relative thermal stability for the different functionals and dispersion corrections.

中文翻译：

---

了解密度泛函选择和范德华处理对预测胺接枝金属有机框架的结合构型、负载和稳定性的影响

金属有机框架 (MOF) 是晶体三维结构，具有高表面积和可调孔隙率。给定 MOF 由通过有机连接体连接的金属节点制成，其确切属性由节点和连接体的选择决定。 MOF 有望用于多种应用，包括气体捕获和存储。 M2(4,4'-二氧化联苯-3,3'-二羧酸酯)——下文简称为 M2(dobpdc)，其中 M = Mg、Mn、Fe、Co、Ni、Cu 或 Zn——被认为是最有前途的材料之一用于二氧化碳捕获应用的结构。用二胺或四胺对 MOF 进行进一步改性可以显着提高气体种类选择性，这是直接空气捕获 CO2 中超稀 CO2 浓度的必要条件。潜在的二胺和四胺有无数种，为探索大量潜在吸附剂的二氧化碳吸附特性铺平了道路。 MOF 孔中胺的数量及其构型是 CO2 吸附能力和动力学的关键驱动因素，因此需要对这些量的计算预测进行验证，以便在胺官能化吸附剂的发现和筛选中适当使用计算方法。在这项工作中，我们研究了密度泛函理论的预测准确性以及一种二胺和两种四胺的胺负载和构型的相关计算。特别是，我们探索了 Perdew-Burke-Ernzerhof (PBE) 泛函及其固体公式 (PBEsol)，有或没有 Grimme-D2 和 Grimme-D3 成对校正（PBE+D2/3 和 PBEsol+D2/3），两个修订后的 PBE 泛函，具有 Grimme-D2 和 Grimme-D3 成对校正（RPBE+D2/3 和 revPBE+D2/3），以及非局部范德华相关 (vdW-DF2) 泛函。我们还研究了通用图深度学习原子间势 (M3GNet) 对加载和配置的预测准确性。这些结果使我们能够确定一种用于配置预测的有用筛选程序，该程序具有用于快速评估的粗略组件和用于详细分析的更高精度组件。我们的一般观察是，基于神经网络的潜力可以用作高级和快速筛选工具，而 PBEsol+D3 可以在所有研究的系统中提供完全定性的预测图片，因此可以用于高精度主题预测。最后，我们简要探讨了不同泛函和色散校正的相对热稳定性的预测。