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Zeolite Structure 1
SAMPCloud_webpages/photos/323_3x3x3.jpg
ICSD 323.
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Zeolite Structure 2
SAMPCloud_webpages/photos/40128_3x3x3.jpg
ICSD 40128.
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Zeolite Structure 3
SAMPCloud_webpages/photos/40134_3x3x3.jpg
ICSD 40134.
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Zeolite Structure 4
SAMPCloud_webpages/photos/40136_3x3x3.jpg
ICSD 40136.
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Zeolite Structure 5
SAMPCloud_webpages/photos/40137_3x3x3.jpg
ICSD 40137.
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Zeolite Structure 6
SAMPCloud_webpages/photos/40138_3x3x3.jpg
ICSD 40138.
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Zeolite Structure 7
SAMPCloud_webpages/photos/40139_3x3x3.jpg
ICSD 40139.
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Zeolite Structure 8
SAMPCloud_webpages/photos/100095_3x3x3.jpg
ICSD 100095.
During this project we developed machine learning models that allow to
classify zeolite crystals according to their framework type.
Zeolites are microporous crystalline
materials with highly regular framework structures consisting of molecular-sized
pores and channels. The characteristic framework type of a zeolite is conventionally
defined by combining information on its coordination sequences, vertex symbols, tiling,
and transitivity information. Our categorization model, the
Zeolite Structure
Predictor (ZSP), is based on the Random Forestâ„¢ algorithm and uses a nine-dimensional feature vector including topological descriptors obt
ained by computational geometry techniques, together with selected physical and chemical properties of zeolite crystals. Trained on the cr
ystallographic structures of known zeolites from the Inorganic Crystal Structure Database, this model predicts the framework types of zeol
ite crystals with
98% accuracy.
In addition we developed a cloud-based computing system in the Windows Azure cloud
that allows users to use the ZSP model through a Web browser. This automated system permits a user to calculate the feature vector used by
ZSP. The workflow is named "SAMPCloud" and integrates executables in Fortran and Python for number crunching with Weka for machine learning
and Jmol for Web-based atomistic visualization in an interactive compute system accessed through the Web (see figure on left). SAMPCloud is robust, easy to us
e, and open source. Communities of scientists, engineers, and students knowledgeable in Windows-based computing will find this new workflo
w attractive and easy to be implemented in scientific scenarios in which the developer needs to combine heterogeneous software components.
SAMPCloud is open source. The source code can be downloaded here or at Sourceforge