Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning
Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning
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DOI：
10.1021/acs.chemmater.7b02532
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发表时间：
2017-09-26
影响因子：
8.6
通讯作者：
Couder, Francois-Xavier
中科院分区：
文献类型：
--
作者：
Evans, Jack D.;Couder, Francois-Xavier
我们在这里表明，机器学习是预测沸石弹性响应的一个强大的新工具。我们建立了我们的机器学习方法，仅依赖于几何特征，这与沸石的局部几何形状，结构和孔隙率有关，以预测沸石的体积和剪切模量，其精度超过力场方法。该模型的发展已经说明了沸石的特征和弹性模量之间的明确的相关性，提供了对沸石骨架力学的特殊见解。最后，我们采用这种方法来预测590 448假设沸石的弹性响应，这个庞大的数据库的结果提供了明确的证据，在多孔材料的稳定性趋势。
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.