Zeolites investigation by machine learning

The purpose of this study is to find out if there are any ways to create synthetic zeolites base on the only information in topological features, pore volume, topological density and unit cell parameter to achieve the require type of zeolite material which may contribute to new discovery of zeolite...

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Main Author: Loh, Yu Wei
Other Authors: Su Haibin
Format: Final Year Project
Language:English
Published: 2016
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Online Access:http://hdl.handle.net/10356/67304
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-673042023-03-04T15:36:01Z Zeolites investigation by machine learning Loh, Yu Wei Su Haibin School of Materials Science and Engineering DRNTU::Engineering The purpose of this study is to find out if there are any ways to create synthetic zeolites base on the only information in topological features, pore volume, topological density and unit cell parameter to achieve the require type of zeolite material which may contribute to new discovery of zeolite structure. However, most people produce the type of zeolite materials are based on the elements that are present and neglect certain properties to achieve new recipes for new zeolite structure or the structure they wanted. Due to the facts that some of the properties can be adjusted or controlled during the synthesis of synthetic zeolites to fit into different application, people tends to overlook on such properties which are amendable. This report covers on how significant of those properties are effecting the classification of the type of zeolite material which would relate to the new discovery of zeolite structure in the future. Experiment and investigate are done by using machine learning. Specific algorithm is used to obtain accurate prediction results. The finding and results discovered in this project has proved that topological features, pore volume, topological density and unit cell parameter properties hold some significant contribution toward the synthesis to achieve the required type of synthetic zeolite material, although they are not as highly significant as the element presences. The limitation of this project is that all investigates are done by using machine learning and not by the actual hand-on experiment. This could affect the differences between theoretical and physical results. However the advance knowledge from machine learning could provide a good head start in doing any physical experiment if required. Bachelor of Engineering (Materials Engineering) 2016-05-15T01:14:31Z 2016-05-15T01:14:31Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67304 en Nanyang Technological University 49 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Loh, Yu Wei
Zeolites investigation by machine learning
description The purpose of this study is to find out if there are any ways to create synthetic zeolites base on the only information in topological features, pore volume, topological density and unit cell parameter to achieve the require type of zeolite material which may contribute to new discovery of zeolite structure. However, most people produce the type of zeolite materials are based on the elements that are present and neglect certain properties to achieve new recipes for new zeolite structure or the structure they wanted. Due to the facts that some of the properties can be adjusted or controlled during the synthesis of synthetic zeolites to fit into different application, people tends to overlook on such properties which are amendable. This report covers on how significant of those properties are effecting the classification of the type of zeolite material which would relate to the new discovery of zeolite structure in the future. Experiment and investigate are done by using machine learning. Specific algorithm is used to obtain accurate prediction results. The finding and results discovered in this project has proved that topological features, pore volume, topological density and unit cell parameter properties hold some significant contribution toward the synthesis to achieve the required type of synthetic zeolite material, although they are not as highly significant as the element presences. The limitation of this project is that all investigates are done by using machine learning and not by the actual hand-on experiment. This could affect the differences between theoretical and physical results. However the advance knowledge from machine learning could provide a good head start in doing any physical experiment if required.
author2 Su Haibin
author_facet Su Haibin
Loh, Yu Wei
format Final Year Project
author Loh, Yu Wei
author_sort Loh, Yu Wei
title Zeolites investigation by machine learning
title_short Zeolites investigation by machine learning
title_full Zeolites investigation by machine learning
title_fullStr Zeolites investigation by machine learning
title_full_unstemmed Zeolites investigation by machine learning
title_sort zeolites investigation by machine learning
publishDate 2016
url http://hdl.handle.net/10356/67304
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