As zeolite become more and more important in the society, the demand of zeolite greatly increased. Thousands of theoretical zeolite structures are suggested, only a few of them come into the world. The development of zeolite is still on the process. People try to come up with various ways to categorize zeolite for the ease of research and functionality. Data mining is a process that making use of artificial intelligent to find out the hidden rule of big bunch of data. It has been widely used in every industry. In this report we are using the clustering technique in data mining to cluster the different zeolite structures into respective framework type to prove that data mining technique can be used for material clustering. One of the most us...
Materials discovery is critical for dealing with societal problems, but is a tedious process requiri...
The field of porous materials is widely spreading nowadays, and researchers need to read tremendous ...
We show here that machine learning is a powerful new tool for predicting the elastic response of zeo...
As zeolite become more and more important in the society, the demand of zeolite greatly increased. T...
This project aims at comparing two distinct classifiers and their ability to accurately classify zeo...
With zeolites consumption exceeding 3 million tons and hundreds of new zeolites structures are being...
This paper proposes a multi-level approach to data clustering and provides a novel approach to chara...
© 2019 American Chemical Society. Zeolites are porous, aluminosilicate materials with many industri...
Zeolites are porous, aluminosilicate materials with many industrial and “green” applications. Despit...
The use of machine learning for the prediction of physical and chemical properties of crystals based...
Clustering is a division of data into groups of similar objects. Representing the data by fewer clus...
We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whet...
International audienceZeolites are nanoporous alumino-silicate frameworks widely used as catalysts a...
The production of widely used polymers such as polyester currently relies upon the chemical separati...
Fast, empirical potentials are gaining increased popularity in the computational fields of materials...
Materials discovery is critical for dealing with societal problems, but is a tedious process requiri...
The field of porous materials is widely spreading nowadays, and researchers need to read tremendous ...
We show here that machine learning is a powerful new tool for predicting the elastic response of zeo...
As zeolite become more and more important in the society, the demand of zeolite greatly increased. T...
This project aims at comparing two distinct classifiers and their ability to accurately classify zeo...
With zeolites consumption exceeding 3 million tons and hundreds of new zeolites structures are being...
This paper proposes a multi-level approach to data clustering and provides a novel approach to chara...
© 2019 American Chemical Society. Zeolites are porous, aluminosilicate materials with many industri...
Zeolites are porous, aluminosilicate materials with many industrial and “green” applications. Despit...
The use of machine learning for the prediction of physical and chemical properties of crystals based...
Clustering is a division of data into groups of similar objects. Representing the data by fewer clus...
We have analyzed structural motifs in the Deem database of hypothetical zeolites to investigate whet...
International audienceZeolites are nanoporous alumino-silicate frameworks widely used as catalysts a...
The production of widely used polymers such as polyester currently relies upon the chemical separati...
Fast, empirical potentials are gaining increased popularity in the computational fields of materials...
Materials discovery is critical for dealing with societal problems, but is a tedious process requiri...
The field of porous materials is widely spreading nowadays, and researchers need to read tremendous ...
We show here that machine learning is a powerful new tool for predicting the elastic response of zeo...