Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas

Abstract

To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO2 from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO2, N2, and O2) was calculated and estimated in 6013 computation–ready experimental MOF membranes (CoRE–MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas.

A critical review on machine-learning-assisted screening and design of effective sorbents for carbon dioxide (CO2) capture

“…), and chemical characteristics (e.g., the interaction pattern among different atoms, the charge of active sites, the d-band center of active sites, etc.) (Ghiasi et al, 2019;Burns et al, 2020;Shi et al, 2020;Gupta and Li 2022;Situ et al, 2022). The selection of properties is a subtle but critical task that directly determines the performance of modeling.…”

A New Era of Modeling MOF‐Based Membranes: Cooperation of Theory and Data Science

“…[65] The success of computationready MOF database prompted its recent expansion, which now includes more than 12 000 structures furthering the investigation of membrane-based separation performances of experimentally reported MOF structures. [80,81] Similarly, a recent effort has led to the creation of a curated CSD MOF subset with more than 60 000 structures that is regularly updated. [15] The introduction of hypothetical MOF databases enabled scrutiny into not yet synthesized MOFs that can elucidate the potential performance gains over the synthesized ones.…”

Investigation of light gas adsorption by microporous materials using data analysis and decision tree machine learning algorithm

“…Machine Learning (ML) techniques have been used to model and enhance adsorption processes in various fields such as wastewater treatment for pharmaceutical removal (PRASAD et al, 2023), pollutants in general (ZHANG et al, 2023), and coloration (KOOH et al, 2022); air quality, including carbon dioxide (CO2) capture, which is a highly topical issue (SITU et al, 2022;XIE et al, 2023); heterogeneous catalysis (GHANEKAR; DESHPANDE; GREELEY, 2022); and energy demand (NAIT AMAR et al, 2022).…”