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.

This work is devoted to the development of quantitative structure–property relationship (QSPR) models using machine learning to predict CO2 working capacity and CO2/H2 selectivity for precombustion carbon capture using a topologically diverse database of hypothetical metal–organic framework (MOF) structures (358 400 MOFs, 1166 network topologies). Such a diversity of the networks topology is much higher than previously used (<20 network topologies) for rapid and accurate recognition of high-performing MOFs for other gas-separation applications. The gradient boosted trees regression method allowed us to use 80% of the database as a training set, while the rest was used for the validation and test set. The QSPR models are first built using purely geometric descriptors of MOFs such as gravimetric surface area and void fraction. Additional models which account for chemical features of MOFs are constructed using atomic property weighted radial distribution functions (AP-RDFs) with a novel normalization to accommodate the size diversity of the MOF database. It is shown that the best models for CO2 working capacities (R 2 = 0.944) and CO2/H2 selectivities (R 2 = 0.872) are built from a combination of six geometric descriptors and three AP-RDF descriptors. However, more important is that our QSPR models can identify top 1000 high-performing MOFs in just top 3000 or 5000 MOFs. This work shows that QSPR modeling can account for the topological diversity of MOFs and accelerate the screening for top-performing MOFs for precombustion carbon capture.

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.

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 CO 2 working capacity and CO 2 /CH 4 selectivity 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 [a]

The dynamic uptake behaviour of a gaseous guest has been observed crystallographically, yielding a unique and ever-changing set of host–guest interactions that will drive the improvement of high-capacity iodine capture materials.