Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning

Abstract : 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 of stability trends in porous materials.
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Jack Evans, François-Xavier Coudert. Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning. Chemistry of Materials, American Chemical Society, 2017, 29 (18), pp.7833-7839. ⟨10.1021/acs.chemmater.7b02532⟩. ⟨hal-02264216⟩

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