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Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning

preprint
submitted on 25.08.2017 and posted on 29.08.2017 by Jack D. Evans, François-Xavier Coudert
We show here that machine learning is a powerful new tool for predicting the elastic response of zeolites. We built our machine learning approach relying on geometric features only, which are related to local geometry, structure and porosity of a zeolite, to predict bulk and shear moduli of zeolites with an accuracy exceeding that of force field approaches. The development of this model has illustrated clear correlations between characteristic features of a zeolite and elastic moduli providing exceptional insight into the mechanics of zeolitic frameworks. Finally, we employ this methodology to predict the elastic response of 590 448 hypothetical zeolites, and the results of this massive database provide clear evidence to stability trends in porous materials.

Funding

PSL project DEFORM, grant ANR-10- IDEX-0001-02

History

Topic

  • Computational chemistry and modeling

Email Address of Submitting Author

jack.evans@chimie-paristech.fr

Email Address(es) for Other Author(s)

fx.coudert@chimie-paristech.fr

Institution

Chimie ParisTech

Country

France

ORCID For Submitting Author

0000-0001-9521-2601

Declaration of Conflict of Interest

no conflict of interest

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