Methodology and Model Developments for Computational Discovery of Nanoporous

Methodology and Model Developments for Computational Discovery of Nanoporous
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Total Pages : 201
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ISBN-10 : OCLC:1306240634
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Book Synopsis Methodology and Model Developments for Computational Discovery of Nanoporous by : Eun Hyun Cho

Download or read book Methodology and Model Developments for Computational Discovery of Nanoporous written by Eun Hyun Cho and published by . This book was released on 2021 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.


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