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.

中文翻译：

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通过机器学习预测沸石骨架的机械性能

我们在这里表明，机器学习是预测沸石弹性响应的强大新工具。我们仅依靠与沸石的局部几何形状，结构和孔隙率有关的几何特征来构建机器学习方法，以预测沸石的体积模量和剪切模量，其准确性超过了力场方法。该模型的开发说明了沸石的特征与弹性模量之间的明确关联，从而为沸石骨架的力学提供了出色的见识。最后，我们采用这种方法来预测590 448种假想沸石的弹性响应，该庞大数据库的结果为多孔材料的稳定性趋势提供了清晰的证据。