Understanding and Predicting the Spatially Resolved Adsorption Properties of Nanoporous Materials

• Published in: Journal of chemical theory and computation Vol. 20; no. 12; pp. 5259 - 5275
• Main Authors: Sun, Yangzesheng, Siepmann, J. Ilja
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 25.06.2024
• Subjects: Adsorbates; Adsorption; Carbon dioxide; Condensed Matter, Interfaces, and Materials; Crystallography; Electric power grids; Hydrogen sulfide; Krypton; Predictions; Statistical thermodynamics; Xenon; Zeolites
• ISSN: 1549-9618; 1549-9626; 1549-9626
• DOI: 10.1021/acs.jctc.4c00149

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. 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.