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 language||English (US)|
|Journal||Journal of Chemical Physics|
|State||Published - Jul 7 2021|
Bibliographical noteFunding Information:
This work was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences through the Nanoporous Materials Genome Center under Award No. DE-FG02-17ER16362. Z.L. acknowledges support from a Data Science Fellowship via the Northwestern Institute on Complex Systems (NICO). R.Q.S. has a financial interest in the startup company NuMat Technologies, which is seeking to commercialize metal–organic frameworks. This research was partly supported through the computational resources provided for the Quest high-performance computing facility at Northwestern University, which is jointly supported by the Office of the Provost, the Office for Research, and Northwestern University Information Technology. We also gratefully acknowledge the resources provided by the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.
© 2021 Author(s).
PubMed: MeSH publication types
- Journal Article