Inverse Design in Porous Materials Using Artificial Neural Networks

12 April 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Generating optimal nanomaterials using artificial neural networks can potentially lead to a significant revolution in future materials design. Although progress has been made in creating small and simple molecules, complex materials such as crystalline porous materials have yet to be generated using any of the neural networks. In this work, we have for the first time implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 14 crystalline porous materials. Our neural network takes in inputs in the form of energy and material dimensions and we show that zeolites with a user-desired range of 4 kJ/mol methane heat of adsorption can be reliably produced using our neural network. The fine-tuning of user-desired capability can potentially accelerate materials development as it demonstrates a successful case of inverse design in porous materials.


machine learning
porous materials
generative adversarial network

Supplementary materials



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