Maciej Haranczyk

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal-organic frameworks. They are of particular interest for gas separation or storage applications, for instance as methane adsorbents for a vehicular natural gas tank or other portable applications.

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are examples of advanced porous polymeric materials that have emerged in recent years. Their crystalline structure and modular synthesis offer unmatched versatility in their design. By exchanging chemical building blocks, one can both explore the unlimited space of possible structural chemistry within an isoreticular (same crystal topology) series, as well as achieve a wide range of alternative topologies.

Occasional, large amplitude flexibility in metal-organic frameworks (MOFs) is one of the most intriguing recent discoveries in chemistry and material science. Yet, there is at present no theoretical framework that permits the identification of flexible structures in the rapidly expanding universe of MOFs. Here, we propose a simple method to predict whether a MOF is flexible, based on treating it as a system of rigid elements, connected by hinges. This proposition is correct in application to MOFs based on rigid carboxylate linkers.

Natural gas, mostly methane, is an attractive replacement of petroleum fuels for automotive vehicles because of its economic and environmental advantages. The technological obstacle to using methane as a vehicular fuel is its comparatively low volumetric energy density, necessitating densification strategies to yield reasonable driving ranges from a reasonably sized tank.

Methane can be an alternative fuel for vehicular usage provided that new porous materials are developed for its efficient adsorption-based storage. Herein, we search for materials for this application within the family of diamond analogues. We used density functional theory to investigate structures in which tetrahedral C atoms of diamond are separated by-CC-or-BN-groups, as well as ones involving substitution of tetrahedral C atoms with Si and Ge atoms.

Porous polymer networks (PPNs) are a class of porous materials of particular interest in a variety of energy-related applications because of their stability, high surface areas, and gas uptake capacities. Computationally derived structures for five recently synthesized PPN frameworks, PPN-2,-3,-4,-5, and-6, were generated for various topologies, optimized using semiempirical electronic structure methods, and evaluated using classical grand-canonical Monte Carlo simulations.

Metal-organic framework (MOF) crystal topologies which permit the highest internal surface areas are identified by means of multiobjective optimization and abstract structure models. We demonstrate that MOF design efforts can be focused within five underlying nets to engineer distinct, Pareto-optimal compromises between high gravimetric and high volumetric surface area materials.

A number of algorithms to analyze crystalline porous materials and their porosity employ the Voronoi tessellation, whereby the space in the material is divided into irregular polyhedral cells that can be analyzed to determine the pore topology and structure. However, the Voronoi tessellation is only appropriate when atoms all have equal radii, and the natural generalization to structures with unequal radii leads to cells with curved boundaries, which are computationally expensive to compute.

Crystalline porous materials have many applications, including catalysis and separations. Identifying suitable materials for a given application can be achieved by screening material databases. Such a screening requires automated high-throughput analysis tools that characterize and represent pore landscapes with descriptors, which can be compared using similarity measures in order to select, group and classify materials. Here, we discuss algorithms for the calculation of two types of pore landscape descriptors.

Metal–organic frameworks (MOFs) are a class of porous materials constructed from metal or metal oxide building blocks connected by organic linkers. MOFs are highly tunable structures that can in theory be custom designed to meet the specific pore geometry and chemistry required for a given application such as methane storage or carbon capture. However, due to the sheer number of potential materials, identification of optimal MOF structures is a significant challenge.

Metal–organic frameworks (MOFs), a class of porous materials, are of particular interest in gas storage and separation applications due largely to their high internal surface areas and tunable structures. MOF-5 is perhaps the archetypal MOF; in particular, many isoreticular analogues of MOF-5 have been synthesized, comprising alternative dicarboxylic acid ligands. In this contribution we introduce a new set of hypothesized MOF-5 analogues, constructed from commercially available organic molecules.

Crystalline porous materials can be exploited in many applications. Discovery of materials with optimum adsorption properties typically involves expensive brute-force characterization of large sets of materials. An alternative approach based on similarity searching that enables discovery of materials with optimum adsorption for CO₂ and other molecules at a fraction of the cost of brute-force characterization is demonstrated.

This work was featured on the front cover of the journal, available here: http://onlinelibrary.wiley.com/doi/10.1002/cphc.201290074/abstract

Large-scale computational screening of thirty thousand zeolite structures was conducted to find optimal structures for separation of ethane/ethene mixtures. Efficient grand canonical Monte Carlo (GCMC) simulations were performed with graphics processing units (GPUs) to obtain pure component adsorption isotherms for both ethane and ethene. We have utilized the ideal adsorbed solution theory (IAST) to obtain the mixture isotherms, which were used to evaluate the performance of each zeolite structure based on its working capacity and selectivity. In our analysis, we have determined that specific arrangements of zeolite framework atoms create sites for the preferential adsorption of ethane over ethene. The majority of optimum separation materials can be identified by utilizing this knowledge and screening structures for the presence of this feature will enable the efficient selection of promising candidate materials for ethane/ethene separation prior to performing molecular simulations.

One of the main bottlenecks to deploying large-scale carbon dioxide capture and storage (CCS) in power plants is the energy required to separate the CO₂ from flue gas. For example, near-term CCS technology applied to coal-fired power plants is projected to reduce the net output of the plant by some 30% and to increase the cost of electricity by 60–80%. Developing capture materials and processes that reduce the parasitic energy imposed by CCS is therefore an important area of research. We have developed a computational approach to rank adsorbents for their performance in CCS. Using this analysis, we have screened hundreds of thousands of zeolite and zeolitic imidazolate framework structures and identified many different structures that have the potential to reduce the parasitic energy of CCS by 30–40% compared with near-term technologies.

We have developed a high-throughput graphics processing unit (GPU) code that can characterize a large database of crystalline porous materials. In our algorithm, the GPU is utilized to accelerate energy grid calculations, where the grid values represent interactions (i.e., Lennard-Jones + Coulomb potentials) between gas molecules (i.e., CH₄ and CO₂) and materials’ framework atoms. Using a parallel flood fill central processing unit (CPU) algorithm, inaccessible regions inside the framework structures are identified and blocked, based on their energy profiles. Finally, we compute the Henry coefficients and heats of adsorption through statistical Widom insertion Monte Carlo moves in the domain restricted to the accessible space. The code offers significant speedup over a single core CPU code and allows us to characterize a set of porous materials at least an order of magnitude larger than those considered in earlier studies. For structures selected from such a prescreening algorithm, full adsorption isotherms can be calculated by conducting multiple Grand Canonical Monte Carlo (GCMC) simulations concurrently within the GPU.

Developing computational tools that enable discovery of new materials for energy-related applications is a challenge. Crystalline porous materials are a promising class of materials that can be used for oil refinement, hydrogen or methane storage as well as carbon dioxide capture. Selecting optimal materials for these important applications requires analysis and screening of millions of potential candidates. Recently, we proposed an automatic approach based on the Fast Marching Method (FMM) for performing analysis of void space inside materials, a critical step preceding expensive molecular dynamics simulations. This breakthrough enables unsupervised, high-throughput characterization of large material databases. The algorithm has three steps: (1) calculation of the cost-grid which represents the structure and encodes the occupiable positions within the void space; (2) using FMM to segment out patches of the void space in the grid of (1), and find how they are connected to form either periodic channels or inaccessible pockets; and (3) generating blocking spheres that encapsulate the discovered inaccessible pockets and are used in proceeding molecular simulations. In this work, we expand upon our original approach through (A) replacement of the FMM-based approach with a more computationally efficient flood fill algorithm; and (B) parallelization of all steps in the algorithm, including a GPU implementation of the most computationally expensive step, the cost-grid generation. We report the acceleration achievable in each step and in the complete application, and discuss the implications for high-throughput material screening.

Crystalline porous materials have a variety of uses, such as for catalysis and separations. Identifying suitable materials for a given application can, in principle, be done by screening material databases. Such a screening requires automated high-throughput analysis tools that calculate topological and geometrical parameters describing pores. These descriptors can be used to compare, select, group, and classify materials. Here, we present a descriptor that captures shape and geometry characteristics of pores. Together with proposed similarity measures, it can be used to perform diversity selection on a set of porous materials. Our representations are histogram encodings of the probe-accessible fragment of the Voronoi network representing the void space of a material. We discuss and demonstrate the application of our approach on the International Zeolite Association (IZA) database of zeolite frameworks and the Deem database of hypothetical zeolites, as well as zeolitic imidazolate frameworks constructed from IZA zeolite structures. The diverse structures retrieved by our method are complementary to those expected by emphasizing diversity in existing one-dimensional descriptors, e.g., surface area, and similar to those obtainable by a (subjective) manual selection based on materials’ visual representations. Our technique allows for reduction of large sets of structures and thus enables the material researcher to focus efforts on maximally dissimilar structures.