A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal–organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.
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7 July 2021
Research Article|
July 01 2021
Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures
Special Collection:
Computational Materials Discovery
Zhao Li
;
Zhao Li
1
Department of Chemical and Biological Engineering, Northwestern University
, Evanston, Illinois 60208, USA
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Benjamin J. Bucior
;
Benjamin J. Bucior
1
Department of Chemical and Biological Engineering, Northwestern University
, Evanston, Illinois 60208, USA
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Haoyuan Chen
;
Haoyuan Chen
1
Department of Chemical and Biological Engineering, Northwestern University
, Evanston, Illinois 60208, USA
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Maciej Haranczyk
;
Maciej Haranczyk
2
IMDEA Materials Institute
, C/Eric Kandel 2, Getafe 28906, Madrid, Spain
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J. Ilja Siepmann
;
J. Ilja Siepmann
3
Department of Chemistry and Chemical Theory Center, University of Minnesota
, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, USA
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Randall Q. Snurr
Randall Q. Snurr
a)
1
Department of Chemical and Biological Engineering, Northwestern University
, Evanston, Illinois 60208, USA
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Zhao Li
1
Benjamin J. Bucior
1
Haoyuan Chen
1
Maciej Haranczyk
2
J. Ilja Siepmann
3
Randall Q. Snurr
1,a)
1
Department of Chemical and Biological Engineering, Northwestern University
, Evanston, Illinois 60208, USA
2
IMDEA Materials Institute
, C/Eric Kandel 2, Getafe 28906, Madrid, Spain
3
Department of Chemistry and Chemical Theory Center, University of Minnesota
, 207 Pleasant Street SE, Minneapolis, Minnesota 55455, USA
a)Author to whom correspondence should be addressed: [email protected]
Note: This paper is part of the JCP Special Topic on Computational Materials Discovery.
J. Chem. Phys. 155, 014701 (2021)
Article history
Received:
March 19 2021
Accepted:
June 09 2021
Citation
Zhao Li, Benjamin J. Bucior, Haoyuan Chen, Maciej Haranczyk, J. Ilja Siepmann, Randall Q. Snurr; Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures. J. Chem. Phys. 7 July 2021; 155 (1): 014701. https://doi.org/10.1063/5.0050823
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