Quantitative Structure–Property Relationship Models for Recognizing Metal Organic Frameworks (MOFs) with High CO<inf>2</inf>Working Capacity and CO<inf>2</inf>/CH<inf>4</inf>Selectivity for Methane Purification

Metal-organic frameworks (MOFs) can theoretically yield a nearly infinite number of nanoporous materials, which represents a combinatorial design challenge that demands computational tools rather than experimental trial-and-error. Here we report Quantitative Structure–Property Relationship (QSPR) models to identify high-performing MOFs for methane purification solely using geometrical features. The CO2working capacity and CO2/CH4selectivity of ca. 320,000 hypothetical MOF structures was computed at conditions relevant to natural gas purification using grand canonical Monte-Carlo (GCMC) simulations. Using 32,500 MOF structures we calibrated binary decision tree (DT) and support vector machine (SVM) models that can accurately identify high-performing MOFs based on their pore size, void fraction and surface area. DT models yielded guidelines of pore size, void fraction and surface area for designing high-performing materials. The SVM machine learning classifiers could be used to quickly pre-screen MOFs, such that compute intensive GCMC simulations are not performed on all structures. The SVM classifiers were tested on ca. 290,000 MOFs that were not part of the training set and could correctly identify up to 90 % of high-performing MOFs while only flagging a fraction of the MOFs for more rigorous screening. QSPR models constitute efficient computational tools for the virtual screening of large structural libraries and provide rational design rules for the discovery of sorbents for methane purification

Rapid and accurate machine learning recognition of high performing metal organic frameworks for CO<inf>2</inf> capture

In this work, we have developed quantitative structure - property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened. SECTION: Surfaces, Interfaces, Porous Materials, and Catalysi

Phosphonate monoesters as carboxylate-like linkers for metal organic frameworks

Bidentate phosphonate monoesters are analogues of popular dicarboxylate linkers in MOFs, but with an alkoxy tether close to the coordinating site. Herein, we report 3-D MOF materials based upon phosphonate monoester linkers. Cu(1,4-benzenediphosphonate bis(monoalkyl ester), CuBDPR, with an ethyl tether is nonporous; however, the methyl tether generates an isomorphous framework that is porous and captures CO 2 with a high isosteric heat of adsorption of 45 kJ mol -1. Computational modeling reveals that the CO 2 uptake is extremely sensitive both to the flexing of the structure and to the orientation of the alkyl tether. © 2011 American Chemical Society