A Cheminformatics Approach for Zeolite Framework Determination

  • Authors:

  • Shujiang Yang;Mohammed Lach-Hab;Iosif I. Vaisman;Estela Blaisten-Barojas

  • Affiliations:

  • Computational Materials Science Center, George Mason University, MSN 6A2, Fairfax, USA 22030;Computational Materials Science Center, George Mason University, MSN 6A2, Fairfax, USA 22030;Computational Materials Science Center, George Mason University, MSN 6A2, Fairfax, USA 22030 and Department of Computational Biology and Bioinformatics, George Mason University, MSN 5B3, Manassas, ...;Computational Materials Science Center, George Mason University, MSN 6A2, Fairfax, USA 22030 and Department of Computational and Data Sciences, George Mason University, MSN 6A2, Fairfax, USA 22030

  • Venue:
  • ICCS 2009 Proceedings of the 9th International Conference on Computational Science
  • Year:
  • 2009

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Abstract

Knowledge of the framework topology of zeolites is essential for multiple applications. Framework type determination relying on the combined information of coordination sequences and vertex symbols is appropriate for crystals with no defects. In this work we present an alternative machine learning model to classify zeolite crystals according to their framework types. The model is based on an eighteen-dimensional feature vector generated from the crystallographic data of zeolite crystals that contains topological, physical-chemical and statistical descriptors. Trained with sufficient known data, this model predicts the framework types of unknown zeolite crystals within 1-2 % error and shows to be better suited when dealing with real zeolite crystals, all of which always have geometrical defects even when the structure is resolved by crystallography.