Machine-Learning-Based Prediction of Methane Adsorption Isotherms at Varied Temperatures for Experimental Adsorbents

Kim, Seo Yul; Kim, Seung Ik; Bae, Yang‐Seop

doi:10.1021/acs.jpcc.0c01757

Cited by 38 publications

(21 citation statements)

References 58 publications

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“…ML methods have already been successfully demonstrated for modeling physical and transport properties in various systems. − Examples include predicting diffusion of organic compounds in air, binary gas mixtures, , organic compounds in water, − and mixtures of binary solvents and hydrocarbons. , ML studies predicting lithium diffusion through solid-state membranes, activation energies for atomic diffusion on metal surfaces, , and ion transport in nanoparticle-based electrolytes have also been reported . Previous work from our group includes using ANNs and random forest methods to predict self-diffusion coefficients of both LJ fluids and real single component fluids. , We have also demonstrated that ANNs can be used to correct finite-size effects in MD simulations of self-diffusion and Maxwell-Stefan diffusion in binary LJ fluids .…”

Section: Introductionmentioning

confidence: 93%

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

et al. 2021

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The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

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Section: Introductionmentioning

confidence: 93%

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

et al. 2021

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“…Machine learning has already been widely used for predicting gas adsorption in MOFs. To date, most of the reported machine models are limited to one single molecule or a specific pair of molecules, for instance, hydrogen, methane, nitrogen, CO 2 , and CO 2 /H 2 mixtures . Recently, broadening this list of molecules has generated interest while still focusing on small molecules and their mixtures. , Developing models that can rapidly be applied to very large and diverse collections of molecules (e.g., thousands of different species) represents a key means in which modeling could dramatically accelerate the scope of MOFs or similar materials for chemical separations in ways that cannot be accomplished experimentally.…”

Section: Introductionmentioning

confidence: 99%

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

Choi

Tang

et al. 2021

J. Phys. Chem. C

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Adsorption-based separations using metal–organic frameworks (MOFs) are a promising alternative to traditional energy-intensive separation process. Machine learning (ML) methods have been applied to predict large collections of adsorption isotherms in MOFs. Previous ML models, however, focus only on predicting single-component adsorption isotherms of a small number of molecules at a single temperature and lack accuracy in the dilute limit. Here we describe a useful strategy for predicting Henry’s constants and heats of adsorption for a diverse set of molecules in large collections of MOFs. To achieve this, a data set containing 21,195 MOF–molecule pairs with 45 adsorbates in 471 MOFs is generated, and a set of 135 descriptors combining energy and chemical information is developed. Robust ML models are developed to predict Henry’s constants and heats of adsorption after removing physically unfavorable adsorption pairs. The adsorption selectivity of near-azeotropic mixtures at two temperatures (300 and 373 K) is predicted with acceptable accuracy by using the predicted Henry’s constants and heats of adsorption. The ability to make temperature-dependent predictions is important for many practical separation applications. Our work sheds light on important challenges and opportunities for developing accurate models predicting adsorption properties for diverse collection of adsorbates and adsorbents.

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“…A small subset of molecular simulation data can be used to train a machine learning algorithm to screen materials quickly. Computational screening of MOFs has been successfully conducted for several applications. , Machine learning algorithms have been shown to have high accuracy and can significantly assist in identifying candidate materials faster and cheaper. , …”

Section: Introductionmentioning

confidence: 99%

“…16,17 Machine learning algorithms have been shown to have high accuracy and can significantly assist in identifying candidate materials faster and cheaper. 18,19 Because of the large number of potential MOFs, it is likely that there are high-performing materials that have yet to be discovered. There are various databases which are comprised of hypothetical materials that have been postulated, yet there are still more many more materials yet to be studied.…”

Section: Introductionmentioning

confidence: 99%

In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption

Beauregard

Pardakhti

Srivastava

2021

J. Chem. Inf. Model.

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The increased use of transition fuels, such as natural gas, and the resulting increase in methane emissions have resulted in a need for novel methane storage materials. Metal−organic frameworks (MOFs) have shown promise as efficient storage materials. A virtually limitless number of potential MOFs can be hypothesized, which exhibit a wide variety of different structural and chemical characteristics. Because of the numerous possibilities, identification of the best MOF for methane storage can be a potentially challenging problem. In this work, determination of the best such MOF was cast as an inverse function problem. The function, a random forest (RF) model using 12 structural and chemical descriptors, was trained on 10% of a data set consisting of 130 398 hypothetical MOFs (hMOFs) to predict simulated methane uptake. The RF model was tested on the remaining 90% of the data. After validation, a genetic algorithm (GA) was used to evolve in silico the best MOFs for methane adsorption. The RF model was imbedded into the GA as the fitness function to predict the methane uptake of the evolved MOFs (eMOFs). The best 15 eMOFs matched hMOFs found in the top 1% of the database. Nine of the 15 eMOFs were found in the top 0.1%. More impressively, two of the eMOFs matched the top two hypothetical MOFs with the highest methane uptake values out of the entire database of 130 398 MOFs. Further, by leveraging the ensemble nature of the GA, it was possible to characterize the importance of the different material properties for methane adsorption, providing fundamental insight for future material design strategies.

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Machine-Learning-Based Prediction of Methane Adsorption Isotherms at Varied Temperatures for Experimental Adsorbents

Cited by 38 publications

References 58 publications

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids

Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

In Silico Evolution of High-Performing Metal Organic Frameworks for Methane Adsorption

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