High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters

Accurate evaluation of adsorbent materials’ performance requires carrying out process simulations that take an analytical isotherm model as an input. In this work, we report a machine learning (ML) approach to approximate the saturation loading of nanoporous materials, an essential parameter for modeling the adsorption-based process simulation. Large-scale grand canonical Monte Carlo (GCMC) simulations were carried out to compute the single-component isotherms for Xe and Kr from the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF) Database 2019. The generated data were used to fit the Langmuir model equation to obtain the saturation loading parameters, which were used as a basis to train several ML models. The performance of trained ML models was then compared with the pore volume-based approach, typically used in the literature, to approximate the saturation loading of the adsorbent material. Ideal vacuum swing adsorption (IVSA) simulations were carried out to screen a large number of MOFs. We found that the ML model better estimates the saturation loading from the curve fitting compared to the pore volume approach. Finally, we carried out high-fidelity vacuum swing adsorption simulations on 15 Xe-selective MOFs. While the IVSA approach provides quantitative information about the process performance metrics, we found that the commonly used performance metrics, such as Xe/Kr IAST selectivity, work as well as the shortcut methods (IVSA simulation) in ranking the adsorbent materials for Xe/Kr separation

High-Throughput, Multiscale Computational Screening of Metal–Organic Frameworks for Xe/Kr Separation with Machine-Learned Parameters

Accurate evaluation of adsorbent materials’ performance requires carrying out process simulations that take an analytical isotherm model as an input. In this work, we report a machine learning (ML) approach to approximate the saturation loading of nanoporous materials, an essential parameter for modeling the adsorption-based process simulation. Large-scale grand canonical Monte Carlo (GCMC) simulations were carried out to compute the single-component isotherms for Xe and Kr from the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF) Database 2019. The generated data were used to fit the Langmuir model equation to obtain the saturation loading parameters, which were used as a basis to train several ML models. The performance of trained ML models was then compared with the pore volume-based approach, typically used in the literature, to approximate the saturation loading of the adsorbent material. Ideal vacuum swing adsorption (IVSA) simulations were carried out to screen a large number of MOFs. We found that the ML model better estimates the saturation loading from the curve fitting compared to the pore volume approach. Finally, we carried out high-fidelity vacuum swing adsorption simulations on 15 Xe-selective MOFs. While the IVSA approach provides quantitative information about the process performance metrics, we found that the commonly used performance metrics, such as Xe/Kr IAST selectivity, work as well as the shortcut methods (IVSA simulation) in ranking the adsorbent materials for Xe/Kr separation

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

Brunauer–Emmett–Teller Areas from Nitrogen and Argon Isotherms Are Similar

Despite recommendations from the 2015 International Union of Pure and Applied Chemistry (IUPAC) technical report, surface areas of porous materials continue to be characterized by an N2 adsorption isotherm using the Brunauer–Emmett–Teller (BET) method. In this study, we provide the basis for such a practice by carrying out systematic large-scale molecular simulations on homogeneous and heterogeneous model surfaces. Specifically, we investigated the purported “orientational effect” of the N2 molecule on these surfaces. Grand canonical Monte Carlo (GCMC) simulation results from 257 diverse metal–organic frameworks show that the BET areas from Ar and N2 are similar in the range of 250–7500 m2/g with a mean deviation of 4%. Detailed analyses based on the consistency criteria for BET equations reveal that the large deviation (>10%) between the BET areas from Ar and N2 are materials specific and more prone to materials that are not able to satisfy the 3 and 4 consistency criteria. For materials that satisfy all four consistency criteria, the BET areas predicted from Ar and N2 isotherms are comparable

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

Leveraging Machine Learning To Predict the Atmospheric Lifetime and the Global Warming Potential of SF₆ Replacement Gases

The global warming potential (GWP) is a relative measure of the capability of a molecule to trap the Earth’s infrared radiation as heat. The measurement or prediction of the GWP of a molecule is based on two factors: the radiative efficiency and atmospheric lifetime of a molecule. While the calculation of the radiative efficiency of a molecule using the computational chemistry approach, such as density functional theory (DFT), is well-established and robust, the development of a computational approach to estimate the atmospheric lifetime remains challenging and limited to date. In this contribution, we developed a machine learning (ML) approach to estimate a molecule’s atmospheric lifetime and GWP100 based on electronic and geometrical features. We benchmarked the state-of-the-art computational workflow with the developed ML model in estimating the atmospheric lifetime and GWP100. The developed ML model outperforms the existing approach with the mean absolute error values of 0.234 (ML-predicted atmospheric lifetime) and 0.249 (direct ML model for GWP100) compared with 0.535 (Atkinson’s method) and 0.773 (Kazakov et al.) from previous works. The developed models were used to screen >7000 molecules in PubChem and bigQM7 data sets in a search for SF6 replacement gas for the electric industry and identified 84 potential candidates

The Origin of <i>p</i>‑Xylene Selectivity in a DABCO Pillar-Layered Metal–Organic Framework: A Combined Experimental and Computational Investigation

We report high experimental p-xylene (pX) selectivity in a pillar-layered metal–organic framework, DUT-8­(Cu). Vapor- and liquid-phase adsorption experiments were carried out to confirm high pX selectivity and large pX uptakes in DUT-8­(Cu). Grand canonical Monte Carlo simulation results show that the presence of DABCO ligands allows for the packing of pX molecules and is responsible for the pX selective nature of the material. The simulation also suggests that the presence of isooctane solvents in the liquid-phase experiments plays an essential role by lowering the adsorption of other xylene isomers, and leads to increased pX selectivity in the liquid-phase as compared to the vapor phase. Density functional theory simulations show that the preferential arrangement is due to the preferential adsorption of pX on the DABCO ligand and the preferential adsorption of isooctane over other xylene isomers

Development of a General Evaluation Metric for Rapid Screening of Adsorbent Materials for Postcombustion CO₂ Capture

Molecular simulations are combined with macroscopic pressure swing adsorption (PSA) modeling and process optimization to screen ∼2900 metal–organic frameworks (MOFs) for their suitability in separating CO2 from N2 under conditions of interest in postcombustion CO2 capture. The hierarchical screening process eliminates MOFs based on metal price, new heuristics based on the internal energy of adsorption, full PSA modeling and optimization, and other factors. Based on PSA modeling of 190 materials, a general evaluation metric (GEM) is developed that can approximately rank the performance of adsorbent materials as defined by the lowest cost for postcombustion CO2 capture. The metric requires only isotherm data and the N2 internal energy of adsorption. The N2 working capacity is the most important component of the metric, followed by the CO2 working capacity, the CO2/N2 selectivity at desorption conditions, and the N2 internal energy of adsorption. Additional analysis shows that the correlation between the cost of CO2 capture and the GEM is better than that of other existing evaluation metrics reported in the literature. For the most promising MOFs, the cost to capture a tonne of CO2 is estimated to be $30–$40 plus the cost of compressing the CO2 product