Machine learning of isomerization in porous molecular frameworks: exploring functional group pair distance distributions
Machine learning of isomerization in porous molecular frameworks: exploring functional group pair distance distributions
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DOI：
10.1039/d3qi01065a
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发表时间：
2023-07-28
影响因子：
7
通讯作者：
Addicoat，Matthew A.
中科院分区：
文献类型：
--
作者：
Nurhuda，Maryam;Hafidh，Yusuf;Addicoat，Matthew A.
分子框架材料（MFM），包括金属有机框架（MOF）、共价有机框架（COF）及其离散等效物、金属有机多面体（MOP）和多孔有机笼（POCs）是多孔材料，由分子片段组成，以多种拓扑结构之一结合。MFM具有各种各样的潜在和实现的吸附应用。为了设计用于特定应用的理想骨架材料，分子片段的组成不是唯一的因素，但是那些片段的排列也很重要，特别是当片段（分子构建块）被化学官能化并且缺乏对称性时。正如在金属有机框架中所观察到的，当改变结构单元的取向或改变官能团的位置时，柔性和吸收性质可能会有很大的不同。然而，尽管官能团的位置对目标性质有很大的影响，但对官能团排列的研究仅在一小部分MOF结构上进行。在这方面的贡献，我们开发了一个指纹/描述优化功能化的分子框架结构，使用机器学习。我们开始从分子框架结构的角度描述为离散的孔形状的集合。为了描述孔的化学环境，我们推导出一个指纹的基础上发生的成对距离在每个孔的官能团。我们提出了在14个最常见的孔的形状，由ditopic（2-连接）连接器的功能基团的安排的可能性。解释了枚举和识别可能的异构体的方法。最后，展示了该指纹图谱在预测客体分子结合能方面的性能。
Molecular Framework Materials (MFMs), including Metal Organic Frameworks (MOFs), Covalent Organic Frameworks (COFs) and their discrete equivalents, Metal Organic Polyhedra (MOPs) and Porous Organic Cages (POCs) are porous materials, composed of molecular fragments, bound in one of many topologies. MFMs have a wide variety of potential and realised adsorption applications. In order to design an ideal framework material for a particular application, the composition of molecular fragments is not the only factor, but the arrangement of the those fragments is also important, especially when the fragments (molecular building blocks) are chemically functionalized and lack symmetry. As has been observed in metal organic frameworks, the flexibility and absorption properties may differ greatly when altering the orientation of the building units or changing the position of functional groups. However, although the position of the functional groups has a great influence on a targeted property, studies on functional group arrangements have only been performed on a small set of MOF structures. In this contribution, we develop a fingerprint/descriptor for optimising functionalized molecular framework structures using machine learning. We begin from the perspective of a molecular framework structure described as a collection of discrete pore shapes. To describe the chemical environment of the pore, we derive a fingerprint based on the occurrence of pairwise distances between functional groups in each pore. We present the possibilities of functional group arrangements in the 14 most common pore shapes, created by ditopic (2-connected) linkers. The method to enumerate and identify possible isomers is explained. Finally the performance of the fingerprint on predicting guest molecule binding energy is demonstrated.