In Silico Discovery of High Deliverable Capacity Metal–Organic Frameworks

Metal–organic
frameworks (MOFs) are actively being explored as potential adsorbed
natural gas storage materials for small vehicles. Experimental exploration
of potential materials is limited by the throughput of synthetic chemistry.
We here describe a computational methodology to complement and guide
these experimental efforts. The method uses known chemical transformations
in silico to identify MOFs with high methane deliverable capacity.
The procedure explicitly considers synthesizability with geometric
requirements on organic linkers. We efficiently search the composition
and conformation space of organic linkers for 9 MOF networks, finding
48 materials with higher predicted deliverable capacity (at 65 bar
storage, 5.8 bar depletion, and 298 K) than MOF-5 in 4 of the 9 networks.
The best material has a predicted deliverable capacity 8% higher than
that of MOF-5.

金属有机框架（MOFs）正被积极研究作为小型车辆吸附天然气储存材料的潜在候选。由于合成化学的通量限制，潜在材料的实验探索受到制约。本研究提出了一种计算方法，旨在补充并指导这些实验工作。该方法利用已知的化学转化原理在虚拟环境中识别具有高甲烷可交付能力的MOFs。该程序明确考虑了有机连接件的合成可行性与几何要求。我们高效地搜索了9种MOF网络中有机连接件的组成和构象空间，发现48种材料的预测可交付能力（在65 bar储存、5.8 bar耗竭和298 K条件下）高于MOF-5，其中4个网络中的材料表现尤为突出。最佳材料的预测可交付能力比MOF-5高出8%。