Graduate Thesis Or Dissertation

 

Novel Approaches to Porous Material Discovery Public Deposited

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/9s161d438

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  • Uses for materials with a large surface area and high porosity have grown sig-nificantly in recent years. Porous materials have found usage in applications such as separation, gas storage, sensing, purification and more, prompting researchers to find and discover numerous new porous materials to suit a specific purpose. Hundreds of thousands of porous materials exist, and in order to aid in porous material discovery, computational methods are regularly used. A hierarchy of computational methods exist, with the most accurate methods usually being the most time consuming and computationally expensive, while cheaper methods exist that might not have the same accuracy. Here, the goal is to search for methods that (a) provide an accurate way of discovering porous materials and (b) provide a fundamental understanding of the materials that are being considered. First an introduction of porous materials and material discovery is presented, and afterwards three manuscripts are presented: (1) We propose a method to identify and learn building blocks of porous organic cages – termed “eigencages” – which are then used to cluster porous organic cages together based on structural features, (2) experimental adsorption data of metal–organic frameworks (MOFs) is used to identify a subset of MOFs to in-corporate in a sensor array in order to maximize the sensors sensing capabilities, and (3) we finally propose a method which circumvents the hierarchy of computational methods by analyzing already reported data for materials to infer unknown properties of those same materials, using a recommender system. I find that these methods provide valuable information and can be used in tandem with other computational methods in the quest to discover porous materials. Finally, the viability of these methods is scrutinized and con-cluded in the final section, in which I argue that with access to large datasets, the need for computationally expensive methods is lessened when we can get a more fundamental understanding of the materials we work with, and exploit that understanding for our gain.
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