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Flat-histogram Monte Carlo as an Efficient Tool to Evaluate Adsorption Processes Involving Rigid and Deformable Molecules

Published

Author(s)

Matthew Witman, Nathan Mahynski, Berend Smit

Abstract

Monte Carlo simulations are the foundational technique for predicting thermodynamic properties of open systems where the process of interest involves the exchange of particles. Thus, they have been used extensively to computationally evaluate the adsorption properties of nanoporous materials and are critical for the in silico identification of promising materials for a variety of gas storage or chemical separation applications. In this work we demonstrate that a well-known biasing technique, known as "flat-histogram" sampling, can be combined with temperature extrapolation of the free energy landscape to provide significantly more useful thermodynamic information than standard open ensemble MC simulations. Namely, we can accurately compute the isosteric heat of adsorption and number of particles adsorbed for various adsorbates over an extremely wide range of temperatures and pressures from a set of simulations at just one temperature. We have also extended this derivation of the temperature extrapolation to adsorbates with intramolecular degrees of freedom when Rosenbluth sampling is employed. Consequently, the working capacity and isosteric heat can be computed for any given combined temperature/pressure swing adsorption process for a large range of operating conditions with both rigid and deformable adsorbates. Continuous thermodynamic properties can be computed with this technique at very moderate computational cost, thereby providing a strong case for its application to the in silico identification of promising nanoporous adsorbents.
Citation
The Journal of Chemical Physics

Keywords

Flat-histogram Monte Carlo,  Adsorption,  Nanoporous materials

Citation

Witman, M. , Mahynski, N. and Smit, B. (2018), Flat-histogram Monte Carlo as an Efficient Tool to Evaluate Adsorption Processes Involving Rigid and Deformable Molecules, The Journal of Chemical Physics, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=926167 (Accessed November 29, 2021)
Created October 7, 2018, Updated October 12, 2021