Nanoporous Materials Explorer

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Introduction

Nanoporous materials are defined as materials having a porosity at the scale of less than 100 nm and often have pores comparable to the size of individual molecules. This gives rise to a series of unique properties, making nanoporous materials useful for industrially important applications such as gas storage, separations, catalysis, et cetera. A vast number of unique nanoporous materials can be created, varying in chemical composition and pore topology. Thousands such materials have already been synthesized and hundreds of thousand hypothetical materials have been computationally predicted. In addition, a considerable number of computational screening studies have appeared in the literature that examine the potential of nanoporous materials for a series of applications. This has generated a substantial amount of data that cannot be presented efficiently by traditional publications. To address this, the Nanoporous Explorer App provides a platform for the aggregation and presentation of data related to nanoporous materials and their properties in an interactive way. The app aims to ease the access to the available information in a way that was not previously possible and enable the identification of promising materials based on their performance and properties. The data for the Nanoporous Explorer App are predicted, measured, and/or maintained by the Nanoporous Materials Genome Center (NMGC), www.chem.umn.edu/nmgc/index.html. This manual covers the description of the classes of materials and type of properties currently accessible.


Using the Nanoporous Explorer App

The search interface of the Nanoporous Explorer App is built around an interactive 2-dimensional scatter plot. In order to initiate a search and generate a scatter plot, you need to select one or more of the available material and adsorbate classes from the drop down menu at the top of the screen and hit the “Explore Nanoporous Materials” button. The default selection includes all the available material classes and no adsorbates.

Interfacing with the data

Once a scatter plot has been generated based on a set of selected material and adsorbate classes, you can click and drag the mouse cursor on the plot to zoom in on a specific area. When zoomed in, a “reest zoom” button will appear to go back to the default plot size. The sliders on the right of the screen can adjust the pressure and temperature range of the data. You can click on a point in the scatter plot to access a list of materials in the vicinity of the chosen point. The materials close to chosen point will appear as a table below the scatter plot, and you can obtain detailed information about the material by clicking on each material in the table The details page for each material provides all the properties available for this specific material. Such details include pore characteristics and material properties for all the materials as well as adsorption properties (Henry’s constants, adsorption isotherms and the heats of adsorption) for a subset of the materials.

Material Classes

CoRE MOFs

Computation-Ready, Experimental Metal-Organic Framework Database (CoRE MOF Database)

Computation-Ready, Experimental MOF database was constructed by a collaboration within the Nanoporous Materials Genome Center. The database is derived from the Cambridge Structural Database (CSD), which contains a large number of small organic and inorganic molecules as well as periodic structures. After the construction of the database, a large-scale GCMC simulation was carried out on all the structures in the database for methane storage and working capacity [1]

Database Construction Procedure

Four steps were taken to effectively make the crystal structures from the CSD computation-ready:

  1. Removal of free solvents and coordinated solvents.
  2. Judicious retention of charge-balancing ions in the framework atoms to make the crystal charge neutral.
  3. Text Mining to recover structures with no framework disorder.
  4. Manual editing of substantially disordered structures.

An example of the solvent removal and charge retention procedures is provided with accompanying python scripts.

Graph-based representation of Molecular Structures

Solvent removal and ion retention procedures rely on the construction of molecular graph representation of a MOF crystal. To construct the periodic adjacency matrix for each structure the Atomic Simulation Environment NeighborList module is used. Two atoms are considered bonded if the distance between them is less than the sum of their CSD covalent radii plus a skin distance of 0.3 Å. The skin distance is chosen to be slightly smaller than the CSD definition (0.4 Å) such that the terminal atom connected to the metal atom does not form another bond with other nearby atoms. The adjacency matrix is passed to the SciPy connected components module to identify the bonded components in each structure.

Removal of Free Solvents and Coordinated Solvents (Text excerpt from Ref. [1])

All bonded components in the molecular graph of each structure other than the MOF framework and charge-balancing ions were removed. The MOF framework was defined as the highest molecular weight bonded component of the graph. Interpenetrated MOF frameworks were retained by identifying the number of atoms, N, in the largest bonded component in the structure and retaining all additional components having at least 0.5N atoms. The bonded component corresponding to the MOF framework often includes undesirable solvent bound to unsaturated metal centers. To remove these coordinated solvent molecules, we performed a trial “cut” on all bonds between metal centers oxygen atoms. If the number of bonded clusters detected by the connected component algorithm remained constant, the bond was restored. If the number of bonded components increased, the entire new component was considered a solvent molecule and removed. An exception was built into the algorithm to retain hydroxyl groups bonded to metal centers.

Retention of Charge-Balancing Ions (Text excerpt from Ref. [1])

Many MOF structures with associated charge-balancing ions also contain undesirable neutral solvent molecules. To discriminate between ionic species and neutral solvent molecules, the elemental compositions of the bonded components in a molecular graph of each structure were compared to the chemical formulas reported by the CSD using an in-house python script. The bonded components are the independent “molecules” within each structure; these include the MOF framework, the ionic species, and any neutral solvent molecules. The bonded components with elemental compositions matching the composition of the ions reported by the CSD were exempted from deletion in the solvent removal step.

Python Scripts & Example of Cleaning Procedure

To Be Updated

Hypothetical MOFs

Hypothetical Zeolites

Other Hypothetical Materials

Hypothetical zeolites

Material Classes

Henry's Constants

Adsorption Isotherms

Heats of Adsorption

DDEC Point Charges

Pore Descriptors

P-XRD Patterns

References

  1. 1.0 1.1 1.2 Y.G. Chung, J. Camp, M. Haranczyk, B.J. Sikora, W. Bury, V. Krungleviciute, T. Yildirim, O. K. Farha, D. S. Sholl, and R. Q. Snurr, “Computation-Ready, Experimental Metal-Organic Frameworks: A Tool to Enable High-Throughput Screening of Nanoporous Materials,” Chemistry of Materials, 2014, 26 (21), 6185 – 6192