Computational Material Screening Using Artificial Neural Networks for Adsorption Gas Separation

We
present a computationally efficient methodology for screening microporous
materials for adsorption-based gas separation. Specifically, we develop
and employ artificial neural network (ANN)-based surrogate models
that increase the speed of approximating transient adsorption behavior
and breakthrough times by several orders of magnitude without compromising
the predictive capability of a high-fidelity process model. We introduce
the concept of breakthrough event times and develop ANN-based surrogate
models for their accurate prediction. Our results for numerous hypothetical
adsorbents indicate that the effects of different materials-centric
metrics are well-captured by the column breakthrough times at the
process scale, thus providing a scale-bridging measure toward a multiscale
framework for materials screening with process insights. Using the
framework, we also screen the list of existing pure-silica zeolite
frameworks for postcombustion carbon capture and natural gas purification
applications. For postcombustion carbon capture, the top materials
include WEI, JBW, and GIS, and for natural gas purification, the top
materials are GIS, SIV, and DFT. For any binary gas mixture, the developed
ANN models can be leveraged for (i) fundamentally studying the materials
properties that determine the dynamic breakthrough times and gas concentration
profiles and (ii) high-throughput adsorbent screening and identification
of novel materials with desired properties.