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The key to achieving high efficiencies, high performance, and low costs of adsorption heat pumps/chillers (AHPs/ACs) is to choose a suitable adsorbent. A computational screening of 6,013 computation-ready experimental metal–organic...

The capture of low concentration alcohol VOCs (methanol and ethanol) from the air has also attracted more and more attention. In this work, high-throughput computational screening (HTCS) and machine learning (ML) methods based on molecular simulations were used to investigate the adsorption properties of methanol and ethanol in 31 399 hydrophobic metal–organic frameworks (MOFs). First, the structure–performance relationship of MOFs was successfully established through univariate analysis, and the key descriptors identified were LCD and Q 0 st. Five ML methods, Decision Tree (DT), Random Forest (RF), Back Propagation Neural Network (BPNN), Support Vector Machines (SVM), and Tree-based Pipeline Optimization Tool (TPOT), were used to predict the adsorption performance of MOFs. The automatic machine learning (Auto-ML) algorithm TPOT has the best prediction effect on the TSN of methanol and ethanol, with R 2 values of 0.852 and 0.945, respectively. The accuracy of the ML model was further improved using the random search method. Analysis of the algorithms has revealed that GBR and RFR have the highest prediction accuracy and frequency, respectively, for the MOF–methanol and MOF–ethanol systems. Ten MOF materials with excellent adsorption properties (0.002 mol/kg ≥ N CH3OH ≥ 0.001 mol/kg, 0.068 mol/kg ≥ N C2H5OH ≥ 0.016 mol/kg; 420.67 ≥ S CH3OH ≥ 214.29, 3.2 × 106 ≥ S C2H5OH ≥ 8.5 × 103) were selected successfully. After analysis of their adsorption sites, it was found that the primary adsorption sites for methanol and ethanol are located near the amino and halogen groups, and the different metal centers showed great influence on the adsorption capacity of MOFs for two kinds of alcohol molecules through the analysis of their structural commonness. This work can serve as a roadmap for experimental synthesis, innovative design of MOFs, and the development of new ML algorithms.

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.

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