Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning
Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning
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DOI：
10.1021/acs.chemmater.8b02257
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发表时间：
2018-09-25
影响因子：
8.6
通讯作者：
Gomez-Gualdron, Diego A.
中科院分区：
文献类型：
--
作者：
Anderson, Ryther;Rodgers, Jacob;Gomez-Gualdron, Diego A.
开放骨架材料（OFM）如金属有机骨架（MOF）可以提供结构上和化学上可定制的纳米孔。这种特殊的可调性允许在MOF孔内仔细定位最佳吸附位点，以实现选择性CO2物理吸附，使这些材料有望用于节能CO2捕获。然而，考虑到迄今为止合成的数千种MOFs中可以同时改变的众多特征，要阐明提高CO2捕获能力的最关键特征可能是令人生畏的。在这里，我们使用多尺度的方法密度泛函理论（DFT），巨正则蒙特卡罗（GCMC），和机器学习（ML）-调查的作用，各种孔的化学和拓扑结构的功能，在提高CO2捕获指标的MOFs。为了能够彻底“扫描”MOF结构空间的目标区域，我们使用计算合成方法来创建涵盖16种拓扑结构和13种官能化分子构建块的所有可能组合的MOF集合。然后模拟纯CO2和CO2/H-2和CO2/N-2混合物对所得31种母体MOFs及其衍生物的吸附，并计算CO2捕获指标。发现用羟基、巯基、氰基、氨基或硝基化学物质官能化通常改善母体MOF的CO2捕获指标，但这种策略的功效强烈依赖于孔拓扑结构。训练决策树以预测父MOF功能化后二氧化碳捕获指标的改善或下降，而训练另外五种机器学习算法以预测所有MOF的绝对指标。这些算法的训练使我们能够在没有人为偏见的情况下确定各种孔隙化学和结构/拓扑因素对MOF二氧化碳捕获能力的相对重要性。
Open framework materials (OFMs) such as metal-organic frameworks (MOFs) can provide structurally and chemically tailorable nanopores. This exceptional tunability has allowed for careful positioning of optimal adsorption sites within MOF pores to enable selective CO2 physisorption, making these materials promising for energy-efficient CO2 capture. However, given the multitude of features that can be simultaneously altered within the thousands of MOFs synthesized to date, it can be daunting to elucidate the most critical features for boosting CO2 capture capabilities. Here we use a multiscale approach-density functional theory (DFT), grand canonical Monte Carlo (GCMC), and machine learning (ML)-to investigate the role of various pore chemical and topological features in the enhancement of CO2 capture metrics of MOFs. To enable a thorough "sweep" of a target region of MOF structure-space, we used computational synthesis methods to create sets of MOFs encompassing all possible combinations of 16 topologies and 13 functionalized molecular building blocks. The adsorption of pure CO2, and CO2/H-2 and CO2/N-2 mixtures for the resulting 31 parent MOFs and its derivatives was then simulated, and CO2 capture metrics were calculated. Functionalization with hydroxyl, thiol, cyano, amino, or nitro chemistries was found to often improve CO2 capture metrics of the parent MOFs, but the efficacy of this strategy depended strongly on the pore topology. Decision trees were trained to predict the improvement or decline of CO2 capture metrics upon functionalization of parent MOFs, whereas five additional machine learning algorithms were trained to predict absolute metrics for all MOFs. The training of these algorithms allowed us to determine, without human bias, the relative importance of various pore chemical and structural/topological factors on the CO2 capture capabilities of MOFs.