Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures

Zhao Li, Benjamin J. Bucior, Haoyuan Chen, Maciej Haranczyk, J. Ilja Siepmann, Randall Q. Snurr*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number014701
JournalJournal of Chemical Physics
Volume155
Issue number1
DOIs
StatePublished - Jul 7 2021

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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