Nanoporous materials (e.g., zeolite, activated carbon, metal-organic framework, polymeric membranes, etc.) have various technological applications, including gas separation, gas storage, catalytic transformations, etc. The functionalities of nanoporous materials strongly depend on their pore size and shape distribution—which present virtually limitless degrees of freedom. Here, based on high-throughput simulations and machine-learning-based pattern recognition algorithms, we present a framework allowing us to predict the water sorption isotherm of hydrophilic and hydrophobic nanoporous materials. This framework allows us to conduct the inverse design of optimal porous microstructures featuring tailored sorption isotherms.

Modeling of the hydration of nanoporous materials by machine learning

2019 Fall National Meeting

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