Inverse design of porous materials using artificial neural networks

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Generating optimal nanomaterials using artificial neural networks can potentially lead to a notable 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. Here, we have implemented a generative adversarial network that uses a training set of 31,713 known zeolites to produce 121 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 of porous materials.
Publisher
AMER ASSOC ADVANCEMENT SCIENCE
Issue Date
2020-01
Language
English
Article Type
Article
Citation

SCIENCE ADVANCES, v.6, no.1

ISSN
2375-2548
DOI
10.1126/sciadv.aax9324
URI
http://hdl.handle.net/10203/272120
Appears in Collection
CBE-Journal Papers(저널논문)
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