Machine Learning-Enabled Prediction and High-Throughput Screening of Polymer Membranes for Pervaporation Separation

Pervaporation (PV) is considered as a robust membrane-based separation technology for liquid mixtures. However, the development of PV membranes is impeded largely by the lack of adequate models capable of reliably predicting the performance of PV membranes. In this study, we collect an experimental data set with a total of 681 data samples including 16 polymers and 6 organic solvents for a wide variety of water/organic mixtures under various operating conditions. Then, two types of machine learning (ML) models are developed for prediction and high-throughput screening of polymer membranes for PV separation. Based on the intrinsic properties of polymer and solvent (water contact angle of polymer and solubility parameter of solvent) as gross descriptors, the first type accurately predicts PV separation performance (total flux and separation factor). The second type is based on the molecular representation of polymer and solvent, giving accuracy comparable to the first type, and applied to screen ∼1 million hypothetical polymers for PV separation of water/ethanol mixtures. With a threshold of 700 for the PV separation index, 20 polymers are shortlisted, with many surpassing experimental samples. Among these, 10 are further identified to be synthesizable in terms of a synthetic complexity score. The ML models developed in this study would facilitate the optimization of operating conditions and accelerate the development of new polymer membranes for high-performance PV separation

Predictions of Hg⁰ and HgCl₂ Adsorption Properties in UiO-66 from Flue Gas Using Molecular Simulations

Adsorption of mercury species in porous materials is an attractive means to capture these species from flue gas, but the lack of reliable force fields (FFs) for Hg0 and HgCl2 has made modeling adsorption of these important species challenging. We introduce a robust set of FFs to describe interactions between Hg0, HgCl2, and other flue gas components and UiO-66, a prototypical metal–organic framework. Using these FFs, adsorption isotherms in UiO-66 for pure components and mixtures of Hg0, HgCl2, N2, CO2, and SO2 were simulated. HgCl2 has a strong affinity for UiO-66, with a heat of adsorption of ∼60 kJ/mol, whereas Hg0 is only weakly adsorbed. Critically, HgCl2 competes strongly with adsorption of CO2; we give illustrative examples where concentrations of HgCl2 as low as 1 ppm strongly diminish the selectivity and uptake of UiO-66 for CO2 that would be expected for a clean flue gas mixture. These results highlight the potential importance of even trace levels of HgCl2 in consideration of nanoporous adsorbents for CO2 capture in practical settings

Machine Learning-Enabled Prediction and High-Throughput Screening of Polymer Membranes for Pervaporation Separation

Pervaporation (PV) is considered as a robust membrane-based separation technology for liquid mixtures. However, the development of PV membranes is impeded largely by the lack of adequate models capable of reliably predicting the performance of PV membranes. In this study, we collect an experimental data set with a total of 681 data samples including 16 polymers and 6 organic solvents for a wide variety of water/organic mixtures under various operating conditions. Then, two types of machine learning (ML) models are developed for prediction and high-throughput screening of polymer membranes for PV separation. Based on the intrinsic properties of polymer and solvent (water contact angle of polymer and solubility parameter of solvent) as gross descriptors, the first type accurately predicts PV separation performance (total flux and separation factor). The second type is based on the molecular representation of polymer and solvent, giving accuracy comparable to the first type, and applied to screen ∼1 million hypothetical polymers for PV separation of water/ethanol mixtures. With a threshold of 700 for the PV separation index, 20 polymers are shortlisted, with many surpassing experimental samples. Among these, 10 are further identified to be synthesizable in terms of a synthetic complexity score. The ML models developed in this study would facilitate the optimization of operating conditions and accelerate the development of new polymer membranes for high-performance PV separation

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

At present, 100 000+ metal–organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C3H8/C3H6) separation. First, the adsorption capacity and selectivity of C3H8/C3H6 mixture in “Computation-Ready, Experimental” (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental “Cambridge Structural Database” (CSD) MOFs and hypothetical MOFs for C3H8/C3H6 separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes

Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning

At present, 100 000+ metal–organic frameworks (MOFs) have been synthesized, and it is challenging to identity the best candidate for a specific application. In this study, MOFs are rapidly screened via a hierarchical approach for propane/propylene (C3H8/C3H6) separation. First, the adsorption capacity and selectivity of C3H8/C3H6 mixture in “Computation-Ready, Experimental” (CoRE) MOFs are predicted via a molecular simulation (MS) method. The relationships between separation metrics and structural factors are established, and top-performing CoRE MOFs are identified. Then, machine learning (ML) models are trained and developed upon the CoRE MOFs using pore size, pore geometry, and framework chemistry as feature descriptors. By introducing binned pore size distributions and geometric descriptors, the accuracy of ML models is substantially improved. The feature importance of the descriptors is physically interpreted by the Gini impurities and Shapley Additive Explanations. Subsequently, the ML models are used to rapidly screen experimental “Cambridge Structural Database” (CSD) MOFs and hypothetical MOFs for C3H8/C3H6 separation. In the CSD MOFs, the out-of-sample predictions are found to agree well with simulation results, demonstrating the excellent transferability of the ML models from the CoRE to CSD MOFs. Moreover, nine CSD MOFs are identified to possess separation performance superior to top-performing CoRE MOFs. Finally, the similarity and diversity among experimental and hypothetical MOFs are visualized and compared by the t-Distributed Stochastic Neighbor Embedding (t-SNE) feature projections. Remarkably, the CoRE and CSD MOFs are revealed to share a close similarity in both chemical and geometric feature spaces. By synergizing MS and ML, the hierarchical approach developed in this study would advance the rapid screening of MOFs across different databases toward industrially important separation processes

Predictions of Hg⁰ and HgCl₂ Adsorption Properties in UiO-66 from Flue Gas Using Molecular Simulations

Adsorption of mercury species in porous materials is an attractive means to capture these species from flue gas, but the lack of reliable force fields (FFs) for Hg0 and HgCl2 has made modeling adsorption of these important species challenging. We introduce a robust set of FFs to describe interactions between Hg0, HgCl2, and other flue gas components and UiO-66, a prototypical metal–organic framework. Using these FFs, adsorption isotherms in UiO-66 for pure components and mixtures of Hg0, HgCl2, N2, CO2, and SO2 were simulated. HgCl2 has a strong affinity for UiO-66, with a heat of adsorption of ∼60 kJ/mol, whereas Hg0 is only weakly adsorbed. Critically, HgCl2 competes strongly with adsorption of CO2; we give illustrative examples where concentrations of HgCl2 as low as 1 ppm strongly diminish the selectivity and uptake of UiO-66 for CO2 that would be expected for a clean flue gas mixture. These results highlight the potential importance of even trace levels of HgCl2 in consideration of nanoporous adsorbents for CO2 capture in practical settings

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Atmospheric water harvesting based on metal–organic frameworks (MOFs) is an emerging technology to potentially mitigate water scarcity. Because of the tremendously large number of existing MOFs, it is challenging to find suitable candidates. In this context, a data-driven approach to identify top-performing MOFs represents an important direction. Herein, we develop a machine learning (ML) method to predict water adsorption in MOFs and screen out top-performing MOFs for water harvesting. First, experimental water adsorption isotherms in MOFs are collected and water adsorption properties are extracted. Quantitative structure–property relationships are analyzed in terms of pore structure and framework chemistry, providing task-specific design principles. Then, ML models are trained and interpreted to predict water adsorption properties by using structural and chemical features, as well as operating conditions as descriptors. The transferability of the ML models is validated by out-of-sample predictions in seven newly reported MOFs. Finally, the ML models are applied to screen ∼8000 “Computation-Ready, Experimental” (CoRE) MOFs. Top-performing candidates are identified including 149 MOFs with the maximum adsorption capacity ≥35 mmol/g, 39 MOFs with working capacity ≥10 mmol/g in a relative pressure window 0.1–0.3, and 139 MOFs with working capacity ≥8.7 mmol/g in a relative pressure window 0.6–0.9. The developed ML-based method would advance task-oriented design and rapid discovery of reticular materials for energy and environmental applications

Unraveling the Structure–Reactivity Relationship of CuFe₂O₄ Oxygen Carriers for Chemical Looping Combustion: A DFT Study

CuFe2O4 is an emerging high-performance oxygen carrier for chemical looping combustion (CLC), which is hailed as the most promising technology to reduce combustion-derived CO2 emission. CuFe2O4 oxygen carriers with minute structural differences could be largely divergent in the reactivity for the CLC process, which seems not to raise much concern by either experimental or computational studies. Herein, based on density functional theory (DFT) calculations, we compare the performance of three well-documented CuFe2O4 configurations as oxygen carriers in the CLC process and relate the reactivity difference to their structural nuances. The reaction mechanisms of representative CLC reactants (i.e., CH4, H2, and CO) over different CuFe2O4 configurations are explored in-depth. DFT calculations indicate that among different CuFe2O4 configurations, the distribution, orientation, and activity of the O/Cu/Fe sites vary largely over the respective CuFe2O4(100) surfaces, thus affecting the adsorption and oxidation of CLC reactants. Fe atoms, especially in configuration 3, are observed to exhibit a higher degree of exposure and afford lower steric hindrance to interact with CH4 and H2, thereby facilitating higher adsorption energies and lower dissociation energy barriers correspondingly. The Fe–Cu synergistic effect is revealed to promote the dissociation reaction of both CH4 and H2. CO exhibits direct oxidation to CO2 over the O sites, which generally exhibit higher CO binding energies than Cu/Fe sites. Particularly, O sites in configuration 3 are observed with generally lower oxygen vacancy formation energy as well as steric hindrance, thus affording the oxidation of CO in a more facile way. The structure–performance relationship revealed in this work is of positive significance for the design of high-performance spinel CuFe2O4 oxygen carriers

Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening

Atmospheric water harvesting based on metal–organic frameworks (MOFs) is an emerging technology to potentially mitigate water scarcity. Because of the tremendously large number of existing MOFs, it is challenging to find suitable candidates. In this context, a data-driven approach to identify top-performing MOFs represents an important direction. Herein, we develop a machine learning (ML) method to predict water adsorption in MOFs and screen out top-performing MOFs for water harvesting. First, experimental water adsorption isotherms in MOFs are collected and water adsorption properties are extracted. Quantitative structure–property relationships are analyzed in terms of pore structure and framework chemistry, providing task-specific design principles. Then, ML models are trained and interpreted to predict water adsorption properties by using structural and chemical features, as well as operating conditions as descriptors. The transferability of the ML models is validated by out-of-sample predictions in seven newly reported MOFs. Finally, the ML models are applied to screen ∼8000 “Computation-Ready, Experimental” (CoRE) MOFs. Top-performing candidates are identified including 149 MOFs with the maximum adsorption capacity ≥35 mmol/g, 39 MOFs with working capacity ≥10 mmol/g in a relative pressure window 0.1–0.3, and 139 MOFs with working capacity ≥8.7 mmol/g in a relative pressure window 0.6–0.9. The developed ML-based method would advance task-oriented design and rapid discovery of reticular materials for energy and environmental applications