Flat-Histogram Monte Carlo as an Efficient Tool To Evaluate Adsorption Processes Involving Rigid and Deformable...

Area of Science:

• Computational chemistry
• Materials science
• Thermodynamics

Background:

• Monte Carlo (MC) simulations are essential for predicting thermodynamic properties in open systems.
• They are widely used to assess the adsorption characteristics of nanoporous materials for gas storage and separations.
• Standard MC simulations can be computationally intensive for broad thermodynamic predictions.

Purpose of the Study:

• To demonstrate an efficient method for obtaining comprehensive thermodynamic information from MC simulations.
• To accurately compute adsorption properties over wide temperature and pressure ranges from limited simulations.
• To enhance the in silico identification of novel nanoporous adsorbents.

Main Methods:

• Combining "flat-histogram" sampling with temperature extrapolation of the free energy landscape.
• Applying Rosenbluth sampling for adsorbates with intramolecular degrees of freedom.
• Performing simulations at a single temperature to derive properties over a wide range.

Main Results:

• Accurate computation of isosteric heat of adsorption and adsorbed particle numbers for various adsorbates.
• Enables prediction of working capacity and isosteric heat for diverse temperature/pressure swing adsorption processes.
• Achieves continuous thermodynamic property prediction at moderate computational cost.

Conclusions:

• The combined flat-histogram and temperature extrapolation technique significantly enhances the utility of MC simulations.
• This approach offers a computationally efficient pathway for discovering and optimizing nanoporous materials.
• It is highly applicable to in silico screening for gas storage and chemical separation applications.