School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS, UK.
Phys Chem Chem Phys. 2022 May 11;24(18):10906-10914. doi: 10.1039/d2cp00184e.
Tight-binding approaches bridge the gap between force field methods and Density Functional Theory (DFT). Density Functional Tight Binding (DFTB) has been employed for a wide range of systems including proteins, clays and 2D and 3D materials. DFTB is 2-3 orders of magnitude faster than DFT, allowing calculations containing up to 5000 atoms. The efficiency of DFTB comes pre-computed integrals, which are parameterized for each pair of atoms, and the requirement for this parameterization has previously prevented widespread use of DFTB for Metal-Organic Frameworks. The GFN-xTB (Geometries, Frequencies, and Non-covalent interactions Tight Binding) method provides parameters for elements up to ≤ 86. We have therefore employed GFN-xTB to periodic optimizations of the Computation Ready Experimental (CoRE) database of MOF structures. We find that 75% of all cell parameters remain within 5% of the reference (experimental) value and that bonds containing metal atoms are typically well conserved with a mean average deviation of 0.187 Å. Therefore GFN-xTB provides the ability to calculate MOF structures more accurately than force fields, and 2 orders of magnitude faster than DFT. We therefore propose that GFN-xTB is a suitable method for screening of hypothetical MOFs ( ≤ 86), with the advantage of accurate binding energies for adsorption applications.
紧束缚方法在力场方法和密度泛函理论（DFT）之间架起了桥梁。密度泛函紧束缚（DFTB）已被广泛应用于各种系统，包括蛋白质、粘土以及 2D 和 3D 材料。DFTB 比 DFT 快 2-3 个数量级，允许计算包含多达 5000 个原子的系统。DFTB 的效率来自于预先计算的积分，这些积分是针对每一对原子进行参数化的，而这种参数化的要求以前曾阻止了 DFTB 在金属有机骨架（MOF）中的广泛应用。GFN-xTB（几何、频率和非共价相互作用紧束缚）方法为元素提供了高达 ≤86 的参数。因此，我们使用 GFN-xTB 对 MOF 结构的计算就绪实验（CoRE）数据库进行了周期性优化。我们发现，所有晶胞参数中有 75%在 5%的参考值（实验值）范围内，并且含有金属原子的键通常保存得很好，平均平均偏差为 0.187Å。因此，GFN-xTB 能够比力场更准确地计算 MOF 结构，而且比 DFT 快 2 个数量级。因此，我们提出 GFN-xTB 是筛选假设 MOF（≤86）的合适方法，具有吸附应用中准确结合能的优势。
