Nanoporous materials, such as metal-organic frameworks (MOFs), have enormous internal surface areas. Their consequent adsorption properties demonstrate promise towards solving energy-related problems in gas storage and gas separations. Owing to their modular and versatile chemistry, millions of possible nanoporous materials can be synthesized. This vast chemical space allows a material to be tailor-made or fine-tuned to target specific adsorbate molecules and conditions. In this thesis, we utilize molecular models and simulations of gas adsorption in both existing and predicted nanoporous material structures to accelerate the discovery of new materials targeted for gas storage and separations at specific conditions. In the first part of this work, we approach the problem of identifying an optimal porous material to densify natural gas for storage onboard vehicles as fuel. We developed a series of statistical mechanical models to find the thermodynamic parameters that optimize the deliverable capacity of a material. We conclude that the heat of adsorption, which is a commonly used metric to evaluate materials for natural gas storage, is a misleading metric because the optimal heat of adsorption depends on the pore size. Our models also reveal that adsorbate-adsorbate attractions-- in the case where multiple methane molecules can fit into a pore-- can enhance the deliverable capacity. Next, we carried out a high-throughput computational screening of metal-organic frameworks, porous polymer networks, zeolites, and zeolitic imidazolate frameworks for natural gas storage. The data that we collected provide candidate structures for synthesis, reveal relationships between structural characteristics and performance, and suggest that it may be difficult to reach the current Advanced Research Project Agency-Energy (ARPA-E) deliverable capacity target. To assess thermodynamic limits to the methane deliverable capacity, we then built a model of an extreme scenario where an energy field can be created without taking up space with material. This model suggests that, while the failure to reach the ARPA-E storage target is due to material design constraints rather than purely thermodynamic constraints, the ARPA-E storage target is ambitiously close to the thermodynamic limit. In the second part of this work, we approach the problem of identifying a material that selectively adsorbs xenon over krypton. With over half a million nanoporous material structures to consider as candidate adsorbents, the computational cost of a brute-force computational screening strategy was prohibitive. Instead, we employed a machine learning algorithm, a random forest, to learn the relationship between quickly computed structural descriptors and Xe/Kr selectivity, which is more expensive to compute. The trained random forest allowed us to rule out a large percentage of the materials on the basis of quickly-computed structural descriptors. Our machine learning accelerated screening pinpoints top candidates on which to focus experimental efforts and elucidates structure-property relationships for design guidelines for a Xe-selective material. As we are now working with mixed gas adsorption, we developed a user-friendly software package in Python, pyIAST, for ideal adsorbed solution theory (IAST) calculations. IAST is a thermodynamic framework to predict mixed gas adsorption from pure-component adsorption isotherms, which are easier to measure. We provide practical guidelines for applying IAST. Finally, we carry out a high-throughput computational screening of metal-organic frameworks for capturing Xe from air at dilute conditions, a separation encountered in used nuclear fuel reprocessing. Our computational screening, facilitated by a parallelized code on GPUs, predicted a metal-organic framework, SBMOF-1, to be among the most Xe-selective. Our experimental collaborators synthesized and tested SBMOF-1 and found it to exhibit the highest Xe/Kr selectivity and Xe Henry coefficient reported in the literature. Column-breakthrough experiments reveal that SBMOF-1 is a near-term material for capturing xenon from the off-gases of used nuclear fuel reprocessing plants. This is a rare case of a computation-assisted materials discovery.

Our society is currently facing critical energy and environment issues, due to consistent increase in the usage of fossil fuels and anthropogenic activities. One of the viable solutions is to develop better materials to enable more energy-efficient processes for various applications, including gas separations, energy storage, etc. Nanoporous materials, such as zeolites or metal-organic frameworks (MOFs), have drawn considerable attention as promising candidates in these applications. For these materials, their tunability results in essentially infinitely large number of possible candidates. While such vast materials space provides great opportunities, it also imposes a significant challenge on the selection of promising candidates. To this end, data-driven approaches, such as utilizing molecular simulations and machine learning approaches, can play an important role in facilitating the discovery and design of optimum materials. Monte Carlo or molecular dynamics simulation can be utilized to efficiently compute gas adsorption and separation performance of nanoporous materials and therefore could be used to generate big data, which could be challenging timely and monetarily via purely experimental methods. To achieve simulation predictions with high accuracy, it is essential to have molecular models that could accurately represent the gas molecules of interest. For this purpose, we firstly focus on developing a methodology for model developments of small gaseous molecules. Our developed scheme enables an exhaustive and efficient search over all possible model parameters.

This book shows the promising future and essential issues on the storage of the supercritical gases, including hydrogen, methane and carbon dioxide, by adsorption with controlling the gas-solid interaction by use of designed nanoporous materials. It explains the reason why the storage of these gases with adsorption is difficult from the fundamentals in terms of gas-solid interaction. It consists of 14 chapters which describe fundamentals, application, key nanoporous materials (nanoporous carbon, metal organic frame works, zeolites) and their storage performance for hydrogen, methane, and carbon dioxide. Thus, this book appeals to a wide readership of the academic and industrial researchers and it can also be used in the classroom for graduate students focusing on clean energy technology, green chemistry, energy conversion and storage, chemical engineering, nanomaterials science and technology, surface and interface science, adsorption science and technology, carbon science and technology, metal organic framework science, zeolite science, nanoporous materials science, nanotechnology, environmental protection, and gas sensors.

Nanoporous Materials for Molecule Separation and Conversion cover the topic with sections on nanoporous material synthesis and characterization, nanoporous materials for molecule separation, and nanoporous materials for energy storage and renewable energy. Typical nanoporous materials including carbon, zeolite, silica and metal-organic frameworks and their applications in molecule separation and energy related applications are covered. In addition, the fundamentals of molecule adsorption and molecule transport in nanoporous materials are also included, providing readers with a stronger understanding of the principles and topics covered. This is an important reference for anyone exploring nanoporous materials, including researchers and postgraduate students in materials science and chemical engineering. In addition, it is ideal for industry professionals working on a wide range of applications for nanoporous materials. - Outlines the fundamental principles of nanoporous materials design - Explores the application of nanoporous materials in important areas such as molecule separation and energy storage - Gives real-life examples of how nanoporous materials are used in a variety of industry sector

Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer perceptrons -- Local models -- Kernel machines -- Graphical models -- Brief contents -- Hidden markov models -- Bayesian estimation -- Combining multiple learners -- Reinforcement learning -- Design and analysis of machine learning experiments.

Nanoporous materials such as zeolites, zeolitic imidazolate frameworks (ZIFs), and metal-organic frameworks (MOFs) are used as sorbents or membranes for gas separations such as carbon dioxide capture, methane capture, paraffin/olefin separations, etc. The total number of nanoporous materials is large; by changing the chemical composition and/or the structural topologies we can envision an infinite number of possible materials. In practice one can synthesize and fully characterize only a small subset of these materials. Hence, computational study can play an important role by utilizing various techniques in molecular simulations as well as quantum chemical calculations to accelerate the search for optimal materials for various energy-related separations. Accordingly, several large-scale computational screenings of over one hundred thousand materials have been performed to find the best materials for carbon capture, methane capture, and ethane/ethene separation. These large-scale screenings identified a number of promising materials for different applications. Moreover, the analysis of these screening studies yielded insights into those molecular characteristics of a material that contribute to an optimal performance for a given application. These insights provided useful guidelines for future structural design and synthesis. For instance, one of the screening studies indicated that some zeolite structures can potentially reduce the energy penalty imposed on a coal-fired power plant by as much as 35% compared to the near-term MEA technology for carbon capture application. These optimal structures have topologies with a maximized density of pockets and they capture and release CO2 molecules with an optimal energy. These screening studies also pointed to some systems, for which conventional force fields were unable to make sufficiently reliable predictions of the adsorption isotherms of different gasses, e.g., CO2 in MOFs with open-metal sites. For these systems, we developed a systematic, transferable, and efficient methodology to generate force fields by using high-level quantum chemical calculations for accurate predictions of properties. The method was first applied to study the adsorption of CO2 and N2 in Mg-MOF-74, an open-metal site MOF. Two different approaches were developed: one approach based on MP2 calculations on a representative cluster and a second approach based on DFT calculations on a fully periodic MOF. Both approaches gave significantly better predictions of the experimental adsorption isotherms compared to conventional force fields. In addition, we extended the DFT approach to study water adsorption in these materials. Moreover, instead of deriving detailed force fields, we have also proposed an alternative method to efficiently correct initial trial force fields with little information obtained from quantum chemical calculations. Finally, we studied the dynamics of CO2 in Mg-MOF-74 using molecular simulations. This study addressed the dynamic behaviors of CO2 adsorbed in Mg-MOF-74, and provided an alternative explanation to the experimentally measured chemical shifts of 13C labeled CO2 adsorbed in a powder Mg-MOF-74 sample. Our results further illustrated that subtle changes in the topology of frameworks greatly influence CO2 dynamics.

"Molecular Sieves - Science and Technology" covers, in a comprehensive manner, the science and technology of zeolites and all related microporous and mesoporous materials. The contributions are grouped together topically in such a way that each volume deals with a specific sub-field. Volume 7 treats fundamentals and analyses of adsorption and diffusion in zeolites including single-file diffusion. Various methods of measuring adsorption and diffusion are described and discussed.