Machine Learning Approach for Classification of Zeolite Crystals
نویسندگان
چکیده
—A machine learning approach is applied to classify zeolite crystals according to their framework type. The Zeolite-Structure-Predictor is introduced based on the Random Forest algorithm. Zeolites structural data from the Inorganic Crystal Structure Database (ICSD) are used to train the model. The ZSP uses sixteen attributes including topological descriptors obtained with statistical geometry and physical and chemical properties of individual zeolites. Trained with 40 framework types containing at least 5 instances per class, the ZSP can correctly classify zeolites with over 95% accuracy. The performance is shown to improve when more zeolite instances per class are available.
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