Modelling of metal-organic frameworks as tunable adsorbents for separations

Metal-organic frameworks (MOFs) are an interesting class of nanoporous materials synthesized in a “building-block” approach from inorganic nodes and organic linkers. By selecting appropriate building blocks, the structural and chemical properties of the resulting materials can be finely tuned, and this makes MOFs promising materials for applications such as chemical separations, gas storage, sensing, drug delivery, and catalysis. This talk will focus on efforts to design or screen MOFs for adsorption separations. Because of the predictability of MOF synthetic routes and the nearly infinite number of possible structures, molecular modeling is an attractive tool for screening new MOFs before they are synthesized. Large databases of existing and proposed MOFs now exist and can be screened to find the top candidates for a given separation using atomistic Monte Carlo simulations. The resulting data can also provide insight into the molecular-level details that lead to observed macroscopic properties, which can, in turn, be used to design improved candidates. While molecular modeling can predict adsorption properties such as the selectivity and working capacity, process-level modeling can also play a key role in evaluating materials, and we will discuss how the interplay of molecular-level and process-leveling modeling can be used along with experiment to discover, develop, and ultimately design new MOFs for desired separation applications

Efficient identification of hydrophobic MOFs: Application in the capture of toxic industrial chemicals

A novel and quick computational strategy is developed based on water Henry's constants to distinguish different levels of hydrophobicity among metal–organic frameworks. The technique is applied to a large database of MOFs to identify hydrophobic materials.We thank the Army Research Office (grant W911NF-12-1-0130) and the EPSRC IAA Partnership Development Award (RG/75759) for financial support. Computational work was partly supported by Northwestern University's shared computer system, Quest (project: P20261). D. F.-J. thanks the Royal Society for funding through a University Research Fellowship. We also thank Prof. Omar Yaghi and Dr Hiroyasu Furukawa for supplying the experimental water isotherms for some of the MOFs studied in this work. We thank Dr Pritha Ghosh and Dr Diego A. Gomez-Gualdron for fruitful discussions.This is the author accepted manuscript. The final version is available from the Royal Society of Chemistry via http://dx.doi.org/10.1039/c5ta06472

Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications

Metal–organic frameworks (MOFs) are a class of nanoporous material precisely synthesized from molecular building blocks. MOFs could have a critical role in many energy technologies, including carbon capture, separations and storage of energy carriers. Molecular simulations can improve our molecular-level understanding of adsorption in MOFs, and it is now possible to use realistic models for these complicated materials and predict their adsorption properties in quantitative agreement with experiments. Here we review the predictive design and discovery of MOF adsorbents for the separation and storage of energy-relevant molecules, with a view to understanding whether we can reliably discover novel MOFs computationally prior to laboratory synthesis and characterization. We highlight in silico approaches that have discovered new adsorbents that were subsequently confirmed by experiments, and we discuss the roles of high-throughput computational screening and machine learning. We conclude that these tools are already accelerating the discovery of new applications for existing MOFs, and there are now several examples of new MOFs discovered by computational modelling

Rapid Design of Top-Performing Metal-Organic Frameworks with Qualitative Representations of Building Blocks

Data-driven materials design often encounters challenges where systems require or possess qualitative (categorical) information. Metal-organic frameworks (MOFs) are an example of such material systems. The representation of MOFs through different building blocks makes it a challenge for designers to incorporate qualitative information into design optimization. Furthermore, the large number of potential building blocks leads to a combinatorial challenge, with millions of possible MOFs that could be explored through time consuming physics-based approaches. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently without any human intervention. Our approach provides three main advantages: (i) no specific physical descriptors are required and only building blocks that construct the MOFs are used in global optimization through qualitative representations, (ii) the method is application and property independent, and (iii) the latent variable approach provides an interpretable model of qualitative building blocks with physical justification. To demonstrate the effectiveness of our method, we considered a design space with more than 47,000 MOF candidates. By searching only ~1% of the design space, LVGP-MOBBO was able to identify all MOFs on the Pareto front and more than 97% of the 50 top-performing designs for the CO$_2$ working capacity and CO$_2$/N$_2$ selectivity properties. Finally, we compared our approach with the Random Forest algorithm and demonstrated its efficiency, interpretability, and robustness.Comment: 35 pages total. First 29 pages belong to the main manuscript and the remaining 6 six are for the supplementary information, 13 figures total. 9 figures are on the main manuscript and 4 figures are in the supplementary information. 1 table in the supplementary informatio

High-Throughput Screening of Porous Crystalline Materials for Hydrogen Storage Capacity near Room Temperature

The hydrogen storage capabilities of 18,383 porous crystalline structures possessing various degrees of Mg functionalization and diverse physical properties were assessed through combined grand canonical Monte Carlo (GCMC) and quantum mechanical approaches. GCMC simulations were performed for pressures of 2 and 100 bar at a temperature of 243 K. Absolute uptake at 100 bar and deliverable capacity between 100 bar and 2 bar were calculated. Maximum absolute and deliverable gravimetric capacities were 9.35 wt% and 9.12 wt % respectively. Volumetrically, absolute and deliverable capacities were 51 g/L and 30 g/L respectively. In addition, the results reveal relationships between hydrogen uptake and the physical properties of the materials. We show that the introduction of an optimum amount of Mg alkoxide to increase the isosteric heat of adsorption is a promising strategy to improve hydrogen uptake and delivery near ambient temperature.This research was supported by the U.S. Department of Energy (DE-FG02-08EF15967). This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grand No. DGE-0824162 (Y. J. C.). D.F.-J. acknowledges the Royal Society (UK) for a University Research Fellowship. We gratefully acknowledge Northwestern University’s Quest cluster and the National Energy Research Scientific Computing Center’s Carver Cluster for computer resources.This is the accepted manuscript. The final version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/jp4122326

Connecting theory and simulation with experiment for the study of diffusion in nanoporous solids

Nanoporous solids are ubiquitous in chemical, energy, and environmental processes, where controlled transport of molecules through the pores plays a crucial role. They are used as sorbents, chromatographic or membrane materials for separations, and as catalysts and catalyst supports. Defined as materials where confinement effects lead to substantial deviations from bulk diffusion, nanoporous materials include crystalline microporous zeotypes and metal–organic frameworks (MOFs), and a number of semi-crystalline and amorphous mesoporous solids, as well as hierarchically structured materials, containing both nanopores and wider meso- or macropores to facilitate transport over macroscopic distances. The ranges of pore sizes, shapes, and topologies spanned by these materials represent a considerable challenge for predicting molecular diffusivities, but fundamental understanding also provides an opportunity to guide the design of new nanoporous materials to increase the performance of transport limited processes. Remarkable progress in synthesis increasingly allows these designs to be put into practice. Molecular simulation techniques have been used in conjunction with experimental measurements to examine in detail the fundamental diffusion processes within nanoporous solids, to provide insight into the free energy landscape navigated by adsorbates, and to better understand nano-confinement effects. Pore network models, discrete particle models and synthesis-mimicking atomistic models allow to tackle diffusion in mesoporous and hierarchically structured porous materials, where multiscale approaches benefit from ever cheaper parallel computing and higher resolution imaging. Here, we discuss synergistic combinations of simulation and experiment to showcase theoretical progress and computational techniques that have been successful in predicting guest diffusion and providing insights. We also outline where new fundamental developments and experimental techniques are needed to enable more accurate predictions for complex systems

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application

IJTC2006 -12331 ORIGINS OF PRESSURE AND VISCOSITY OSCILLATION WITH FILM THICKNESS IN ULTRA THIN LUBRICATING FILMS

ABSTRACT Both pressure and viscosity have been observed via molecular simulation to oscillate with film thickness in ultra thin lubricating films. This oscillation may need to be considered in lubricated system design for applications that operate in the thin film lubrication regime. Oscillatory behavior occurs when the thickness of the lubricant is on the same order of magnitude as the fluid molecules themselves. It has been suggested by many researchers that this oscillation is related to the layering that occurs in confined fluids. In the present work, this relationship between molecular configuration and oscillation in fluid properties is further investigated. A quantifiable relationship is identified which may enable prediction of oscillatory effects based on fluid atom size and wall separation

The effect of framework flexibility on diffusion of small molecules in the metal-organic framework IRMOF-1

Many efforts have been made to model adsorption and diffusion processes in metalorganic frameworks (MOFs) in the past several years. In most of these studies, the framework has been kept rigid. In this study, we examine the effect of using a flexible framework model on the self-diffusion coefficients and activation energies calculated for several short n-alkanes and benzene in IRMOF-1 from molecular dynamics simulations. We find only minor differences between flexible and rigid framework results. The selfdiffusion coefficients calculated in the flexible framework are 20-50% larger than the ones calculated in the rigid framework, and the activation energies differ by only 10-20%