A Generic Machine Learning Algorithm for the Prediction of Gas Adsorption in Nanoporous Materials
2020
Fanourgakis, George S. | Gkagkas, Konstantinos | Tylianakis, Emmanuel | Froudakis, George
In the present study, we propose a new set of descriptors, appropriate for machine learning (ML) methods, aiming to predict accurately the gas adsorption capacities of nanoporous materials. The present work focuses on systems with nonnegligible electrostatic interactions between the materials’ framework and the guest gas. For that, the CO₂, H₂, and H₂S gases are examined. The present approach is a generalization of our recent development for guest gases with no electrostatic interactions, such as CH₄. For both types of systems, as ML descriptors we consider the adsorption probabilities by the materials’ framework of a small number of probe atoms with different van der Waals diameters. After examination and evaluation of various numerical schemes, probe atoms that carry in their centers an electric dipole are found to be the most appropriate for systems with electrostatic interactions. The accuracy of the present approach is assessed by comparing the ML predictions with a data set of reference results obtained after performing grand canonical Monte Carlo (GCMC) simulations. More specifically, the CO₂, H₂, and H₂S adsorption capacities of the computation-ready, experimental (CoRE) MOFs at several different thermodynamic conditions are considered. The low computational cost for the calculation of the proposed set of ML descriptors allows the screening of very large databases.
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