DOE OSTI · 2860380
Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials
Abstract
Using knowledge from statistical thermodynamics and crystallography, we develop an image–image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate–adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n-butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K. In conclusion, employing a quadratic isotherm model for the local differentiation, SorbIIT yields mean R 2 values of 0.998 for total adsorption and 0.6904 for local adsorption with a resolution of 0.2 Å, and a value of 0.721 for the structural similarity of the local loading distribution.
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Sun, Yangzesheng [Univ. of Minnesota, Minneapolis, MN (United States)] (ORCID:0000000265056473), Siepmann, J. Ilja [Univ. of Minnesota, Minneapolis, MN (United States)] (ORCID:0000000325344507). 2024-04-19. Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials. https://doi.org/10.1021/acs.jctc.4c00149
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